Microbial Chassis Development for Natural Product Biosynthesis

TIBTEC 1884 No. of Pages 18

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Review

Microbial Chassis Development for Natural Product Biosynthesis Xianhao Xu,1 Yanfeng Liu,1 Guocheng Du,1 Rodrigo Ledesma-Amaro

,2 and Long Liu

Engineering microbial cells to efficiently synthesize high-value-added natural products has received increasing attention in recent years. In this review, we describe the pipeline to build chassis cells for natural product production. First, we discuss recently developed genome mining strategies for identifying and designing biosynthetic modules and compare the characteristics of different host microbes. Then, we summarize state-of-the-art systems metabolic engineering tools for reconstructing and fine-tuning biosynthetic pathways and transport mechanisms. Finally, we discuss the future prospects of building next-generation chassis cells for the production of natural products. This review provides theoretical guidance for the rational design and construction of microbial strains to produce natural products.

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*

Highlights Recent advances in omics, in silico modeling analysis and design, and DNA assembly provide big data and various tools to identify, design, and assemble the synthesis modules of natural products. Besides classical strains, various other microorganisms can be used as chassis cells for natural products due to developments in systems biology and synthetic biology. Metabolic engineering based on genetic circuits and novel genome editing tools can optimize the complex pathway of natural products.

The Importance of Constructing Natural Product Chassis Cells Natural products from animals, plants, and microorganisms are wildly used as flavors, nutraceuticals, food additives, cosmetics, and pharmaceuticals [1]. Traditionally, natural products have been extracted from native organisms or made by chemical synthesis, which is usually inefficient, unsustainable, and environmentally unfriendly. In addition, many natural producer organisms are not ideal hosts for bioproduction due to the lack of efficient genetic manipulation techniques, slow growth rates, low yields, or vulnerability to environmental perturbations [2]. Therefore, the heterologous expression of the synthetic pathway for natural product synthesis in microbial chassis cells (see Glossary) has attracted increasing attention. With the development of systems biology and synthetic biology tools for pathway identification, prediction, and reconstruction, some model microorganisms (Escherichia coli, Saccharomyces cerevisiae, Corynebacterium glutamicum, Bacillus subtilis, Streptomyces, and Yarrowia lipolytica) have been identified as ideal chassis for the heterologous expression and large-scale production of high-value natural products [3]. These model microorganisms have advantages as chassis cells because there are a variety of efficient synthetic biology tools to engineer them and they have fast growth rates and well-studied genomes and metabolic networks. However, there are still four major challenges in constructing chassis cells for the efficient synthesis of natural products: (i) identifying genetic production modules; (ii) selecting the most suitable host; (iii) reconstructing and optimizing complex networks; and (iv) efficiently secreting the products. Therefore, systems metabolic engineering strategies have been recently developed to tackle these issues. Recent comprehensive reviews focus on the production of either a specific class of natural products (e.g., terpenoids, aromatic amino acid, flavonoids, antibiotics) [4–7] or the construction of chassis cells for general purposes, which lack the particularities associated with natural product synthesis [8]. Here, we review advances and perspectives for chassis cells built specifically for the production of natural products. This review discusses computational gene identification and pathway design, rational host selection, module reconstitution, metabolic pathway construction and fine-tuning, and product export. Such an approach usually follows the Design-Build-Test-Learn (DBTL) cycle, where every iteration of the cycle allows the improvement of the strain (Figure 1).

1,

Biosensor-based high-throughput screening helps to identify transporters for natural products and facilitate their secretion.

1

Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, Jiangnan University, Wuxi 214122, China 2 Department of Bioengineering and Imperial College Centre for Synthetic Biology, Imperial College London, London SW7 2AZ, UK

*Correspondence: [email protected] (L. Liu).

https://doi.org/10.1016/j.tibtech.2020.01.002 © 2020 Elsevier Ltd. All rights reserved.

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Big Data-Assisted Computation for Gene Identification and Synthetic Module Design At the design stage, identifying the genes involved in natural product biosynthesis is the first step in the construction of chassis cells. Several databases have been established to store information on known metabolic reactions, such as KEGG, MetaCyc, and BRENDA [9–11]. However, many genes involved in natural product synthesis remain unknown. Recent advances in omics technologies and computational biology provide a wealth of data and advanced tools to identify candidate genes. These approaches can be divided into two classes: gene mining of omics data and pathway prediction and design. Depending on the cluster location, spatiotemporal expression, regulatory features, and molecular phylogeny of the candidate genes, various computational strategies have been developed to identify natural product biosynthesis modules [12]. Among them, phylogeny-based gene mining plays the most important role in discovering enzymes and natural products [13]. The big data obtained by omics methods in recent years gives some insight into the evolutionary process of gene sequences and enzymes, thus enabling phylogenetic inference to predict the function of the corresponding enzymes. Accordingly, several computation tools have been developed to identify genes and enzymes involved in natural product synthesis (Table 1). In parallel to the identification of natural genes, an emerging research area, aimed to create new pathways not found in nature, is being developed. Through the expansion of known metabolic reactions and the chemical structures of substrates and products, together with computational frameworks, reaction rules have been generated to exploit alternative routes for natural product synthesis [14–16] (Table 1). Recent advances in directed evolution and de novo computational design of proteins have also provided technical support for the construction of non-natural pathways [14,17]. Additionally, artificial intelligence combined with a Monte Carlo tree search, which is 30 times faster than traditional computer-aided methods, has been used to predict alternative pathways [18]. Artificial intelligence is also a powerful tool to guide the reconstitution of synthetic pathways. In addition to synthetic genes, regulatory elements that control the metabolic flux to the synthetic pathway, such as promoters and ribosomal binding sites (RBSs), are also key parts of the synthesis module. Accordingly, artificial intelligence has been used to guide the design of synthesis modules to maximize bioproduction. For example, MIYA, a machine-learning workflow in conjugation with a DNA assembly strategy, has been used to optimize two heterologous pathways, beta-carotene and violacein. In one study, MIYA was fed with the results from 24 violacein strains and successfully predicted a high-producer strain among 3125 possible designs, which was validated to have 2.42-times improvement [19].

Rational Selection of the Ideal Host Depending on the Natural Product of Choice Selecting a suitable host is essential to achieve high production titers and this selection will depend on the kind of product. Recently, several types of natural products, such as terpenoids, flavonoids, and alkaloids, have been synthesized in microbial cell factories (Box 1). Classic Chassis Cells: E. coli and S. cerevisiae E. coli is the most wildly used prokaryotic chassis for the biosynthesis of chemicals due to its fast growth rate, high productivity, robustness, and wealth of strain manipulation tools [20]. Therefore, several chassis strains of E. coli accumulating key precursor metabolites have been developed, such as acetyl-CoA, malonyl-CoA, mevalonate, and shikimate [8], which are the precursors of terpenoids, polyketides (PKs), flavonoids, aromatic chemicals, and other valuable compounds. 2

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Glossary Artificial intelligence: a computer device mimicking human thought processes, learning capacity, and knowledge storage, which can be used for modeling and database construction. Biosensor: a transcription factor or riboswitch enabling the regulation of cellular processes by responding to a specific signal chemical. Biosensors can be integrated into gene circuits to dynamically regulate gene expression. Chassis cell: a platform cell for the production of a variety of chemicals or enzymes by integrating corresponding synthetic biology modules into the cell. CRISPR/Cas9-based genome editing: a genome editing tool that will cause DNA double strands to break at a target position. Directed evolution: a method of accelerating strain evolution to obtain desired characteristics by creating a selection-pressure environment. Dynamic regulation: a novel synthetic biology strategy to dynamically regulate gene expression using biosensor-based genetic circuits, which can be used to decouple cell growth and product synthesis by chassis cells and reduce the accumulation of intermediate metabolites. Genetic circuit: a regulatory module comprising a signal input part, a controller, and a signal output part. Complex logic gates can be built between different genetic circuits to achieve fine control of gene expression. High-throughput screening: a method for rapid, automated strain screening and detection by introducing a reporting system into the strain. Module: a basic concept in synthetic biology. It aims to divide the complex metabolic network in microbes into different parts to facilitate overall control, including product synthesis modules, competition modules, cell growth modules, and so on. Riboswitch: a mRNA sequence with a specific structure that responds to small molecules and has regulatory functions. The secondary structure of a riboswitch will be changed when combined with specific small molecules, further affecting the transcription and translation of downstream genes. Synthetic biology: a discipline of de novo construction of new cell factories or the redesign of the enzymes, metabolic networks, and regulatory systems of existing organisms to produce specific

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Additionally, many natural products have been biosynthesized in E. coli, such as flavonoids, alkaloids, and terpenoids [21]. These chassis cells and their derivative strains can be used as platforms to produce a specific type of natural product. However, E. coli lacks posttranslational modification systems and intracellular membranes, which limits the heterologous expression of many plant metabolites. Compared with E. coli, S. cerevisiae possesses a relatively complete post-translational modification and intracellular organelle system. Therefore, it can functionally express, for example, plant-derived cytochrome monooxygenases (P450), which are endoplasmic reticulum membrane-bound proteins and essential for the biosynthesis of many products. A paradigmatic case is the semisynthesis of artemisinin, where both E. coli and S. cerevisiae were selected as potential chassis cells and a relatively higher titer of amorphadiene (20 g/l; a precursor of artemisinic acid) was achieved in E. coli [22]. However, only 1 g/l artemisinic acid was produced by E. coli due to the inefficient expression of P450 (CYP71AV1). Therefore, the chassis cell was switched to S. cerevisiae, which finally produced 40 g/l amorphadiene and 25 g/l artemisinic acid [23]. Compared with S. cerevisiae, E. coli has the advantage of shorter doubling times. To combine the advantages of these two strains, a synthetic co-culture method was established to divide the complex metabolic pathways into two parts in the suitable host [24], such as for the biosynthesis of oxygenated taxanes, in which E. coli was responsible for the synthesis of taxadiene and S. cerevisiae was in charge of the oxygenation of taxadiene [25].

chemicals. This discipline focuses on building standardized, modular, and uncoupled components and finally builds or reprograms the artificial biosystem by assembling and integrating these modules.

Nonconventional Chassis Cells: B. subtilis, Streptomyces, C. glutamicum, and Y. lipolytica B. subtilis is a ‘generally regarded as safe’ (GRAS) species with rapid cell growth, wide carbon source utilization capabilities, and efficient secretory function. It is a preferred chassis cell for the production of proteins and nutraceuticals [26]. Recently, it has been used as a chassis cell to produce vitamins and functional sugars, such as vitamin B2, K2, N-acetylglucosamine, and human milk oligosaccharides [27]. Several synthetic biology tools have been established to tune its gene expression and edit its genome [28]. However, the development of synthetic biology tools for B. subtilis lags behind E. coli, as, for example, it lacks a stable gene expression system. Streptomyces is known for its ability to produce a large amount of microbial natural products, especially antibiotics. It is an ideal chassis cell for the discovery and overexpression of microbebased products [29]. A variety of compounds have been produced in Streptomyces, such as PKs, non-ribosomal peptides (NRPs), and terpenes [30–32], which means that Streptomyces can provide sufficient precursors and cofactors for the synthesis of these families of metabolites. Besides, Streptomyces possesses a relatively sophisticated post-translational modification system compared with E. coli, which is essential for the activity of natural products. However, the fermentation and genetic manipulation cycle of Streptomyces is much longer than that of E. coli and S. cerevisiae and most industrial Streptomyces strains are difficult to modify. C. glutamicum is a Gram-positive bacterium widely used for the industrial production of amino acids [33]. It can simultaneously utilize multiple carbon sources because it lacks the carbon catabolite repression regulatory system and it is more tolerant to aromatic compounds than E. coli [33,34]. Because of those advantages along with some recently developed synthetic biology tools, C. glutamicum has been used as a chassis cell to produce aromatic compounds such as shikimate, 4-HBA, and eriodictyol [34–36]. The titer of 4-HBA in C. glutamicum reached 36.6 g/l, which is the highest observed productivity of that compound, surpassing E. coli (12 g/l) and S. cerevisiae (151 mg/l) [34]. However, the genetic manipulation efficiency of C. glutamicum is much lower than that of E. coli and S. cerevisiae. Y. lipolytica is an oleaginous yeast with a high tricarboxylic acid cycle (TCA) cycle metabolic flux and is an ideal strain for producing lipid compounds, such as fatty acids [37]. The highly Trends in Biotechnology, Month 2020, Vol. xx, No. xx

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Figure 1. Scheme of the Workflow for Natural Product Chassis Cell Construction with Synthetic Biology Tools. The workflow includes pathway identification and design, host selection, metabolic flux optimization, and product exportation. (A) Pathway identification and design rely on genome data, mining tools, and pathway design strategies obtained or developed in recent years. (B) Not just model microorganisms but also some nonconventional microorganisms can serve as chassis cells for natural products. When selecting a host, consideration should be given to the characteristics of the product, the host’s tolerance to the product, the host’s genetic manipulation tools, genetic databases, growth characteristics, and the ability to utilize cheap materials. (C) Recently developed synthetic biology tools will accelerate the optimization of synthesis pathways for products. (D) Rational design and directed evolution tools were developed to achieve maximum exportation of natural products.

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Table 1. Computational Strategies and Tools in the Construction of Chassis Cells for Natural Products Name

Description

Refs

NP.searcher

Website for the prediction of gene clusters of PKs and NRPs in genome sequences (http://dna.sherman.lsi.umich.edu)

[133]

CoExpNetViz

Website allows researchers to submit their transcriptomics data for cross-species coexpression analysis and to find whether the gene may have similar functions and regulation (http://bioinformatics.psb.ugent.be/webtools/coexpr/)

[134]

ATTED-II

Database of coexpressed genes, can be used to predict coregulated gene groups (http://www.atted.bio.titech.ac.jp)

[135]

PRISM 3

Web server for the prediction of genetically encoded NRPs, type I and II PKs, aminocoumarins, antimetabolites, bisindoles, and phosphonates based on chemical graphs (http://magarveylab.ca/prism/)

[136]

antiSMASH50

Web server for the identification and analysis of 52 types of biosynthetic gene clusters in bacteria, fungi, and plants (https://antismash.secondarymetabolites.org)

[137]

DeepBGC

Deep learning strategy to reduce false-positive rates of biosynthetic gene cluster (BGC) identification and improve the ability to discover novel BGC classes compared with antiSMASH (https://github.com/Merck/deepbgc)

[138]

RODEO

Algorithm developed to identify ribosomally synthesized and post-translationally modified peptide (RiPP) BGCs (http://www.ripprodeo.org)

[139]

RiPPER

Method for family-independent identification of the precursor peptides of RiPPs (https://github.com/streptomyces/ripper)

[140]

IMG-ABC

Database of experimentally verified and predicted BGCs across 40 000 isolated microbial genomes (https://img.jgi.doe.gov/abc/)

[141]

Genome mining

Pathway prediction and design ATLAS

Database of all theoretical biochemical reactions based on known biochemical principles and compounds (http://lcsb-databases.epfl.ch/atlas/)

[142]

RetroPath2.0

Open-source retrosynthesis workflow for pathway prediction based on generalized reaction rules (https://github.com/brsynth/rp2paths)

[16]

rePrime/novoStoic

rePrime generates reaction rules from a database containing a known reaction and then delivers them to novoStoic to predict novel reactions based on mass balance and Gibbs free-energy change

[143]

MRE

Open web server for pathway design; this tool focuses on suggestions of which foreign enzymes and pathways could maintain effective activity in host cells by considering endogenous pathways. (http://www.cbrc.kaust.edu.sa/ mre/)

[15]

3N-MCTS

Pathway design method based on Monte Carlo trees and artificial intelligence

[18]

TransportDB 2.0

Relational database containing information on predicted transporters of organisms whose complete genome sequences are available (http://www.membranetransport. org/transportDB2/index.html)

[80]

TCDB

Transporter database containing information on more than 10 000 transporters from all types of organism, was the only database approved by the International Union of Biochemistry and Molecular Biology (IUBMB) (http://www.tcdb.org/)

[81]

VARIDT 1.0

Drug transporter database containing information on 177 confirmed drug transporters (http://varidt.idrblab.net/ttd/)

[82]

MemProtM

Database of membrane protein structure; these structures were embedded in lipid bilayers (http://memprotmd.bioch.ox.ac.uk)

[144]

Transporter database

active TCA cycle provides large amounts of acetyl-CoA and malonyl-CoA for the synthesis of natural products. Y. lipolytica has been engineered to expand its substrate usage and utilize low-cost carbon sources for bioproduction [38]. Meanwhile, as a eukaryote, it also has intracellular organelles. In addition, numerous synthetic biology tools have been recently

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developed for Y. lipolytica, facilitating its manipulation [39]. Therefore, it is a suitable chassis cell for the synthesis of plant-derived terpenoids, PKs, and flavonoids [40]. For instance, the highest production described of the valuable carotenoid beta-carotene (6 g/l) has been achieved in this host [41]. However, the high nonhomologous end-joining probability affects gene-targeted integration efficiency in Y. lipolytica and it lacks a stable plasmid expression system [42]. Besides these nonconventional chassis organisms, many other microbes are being studied for their ability to produce natural products, such as Ashbya gossypii [43,44], Kluyveromyces marxianus [45], Rhodosporidium toruloides [46], Vibrio natrigens [47], Zymomonas mobilis [48], Pseudomonas chlororaphis [49], Mucor circinelloides [50], and Pseudomonas putida [51]. In conclusion, it is important to choose the most suitable host based on the characteristics of the natural product of choice. First, an organism that produces enough precursor molecules for the pathway of interest or that is already good at producing the natural product should be sought. Second, it is important to have some knowledge about the genome and metabolic network of the host, and it must be susceptible to being engineered using synthetic biology tools with sufficient efficiency. Third, if the heterologous pathway to be expressed contains organelleembedded enzymes, a eukaryotic microorganism may be preferred. Fourth, if the product of choice or some intermediate is toxic, the tolerance of the host to these compounds must be considered. Finally, the overall process cycle must be considered to make the process more affordable. Organisms with the ability to use inexpensive substrates and grow quickly, with reduced purification costs, would be desirable.

Reconstituting, Integrating, and Optimizing Complex Heterologous Pathways After designing the biosynthesis pathway and selecting a suitable host for pathway of choice, the chassis cell must be built. There are three major challenges in this stage. First, since genes for natural product synthesis are often situated together to form large gene clusters, it is important to obtain and integrate such clusters into the genome of the host and to efficiently modulate Box 1. Natural Products Generally, natural products refer to organic compounds found in nature that have medical or physiological functions. In a narrower sense, this term refers to secondary metabolites from animals, plants, and microorganisms, including carbohydrates, steroids, terpenoids, alkaloids, PKs, aromatic chemicals, and NRPs (Figure I). Carbohydrates can be divided into monosaccharides, oligosaccharides, and polysaccharides according to their degree of polymerization, such as N-acetylneuraminic acid, 2′-fucosyllactose, and hyaluronic acid. N-Acetylneuraminic acid and 2′-fucosyllactose are crucial for infant development and hyaluronic acid is widely used in cosmetic applications. These products are derived from central carbon metabolism and have achieved heterologous synthesis in microorganisms. Among these strains, Bacillus subtilis is generally regarded as a safe strain and is widely used for the synthesis of functional sugars. Both steroids and terpenoids are synthesized from the mevalonate or 2-C-methyl-D-erythritol-4-phosphate pathway and are widely used as drugs (artemisinin and campesterol), fragrances (limonene), and food additives (carotenoids). Compared with other microorganisms, yeast is more suitable as a chassis cell for steroid and terpenoid synthesis because this type of natural product is mainly derived from plants. Ergosterol, the precursor of vitamin D, is the main sterol and a component of the cell membrane in Saccharomyces cerevisiae. Therefore, ergosterol is commercially produced by cultivating yeast. Meanwhile, the titer of farnesene (the precursor of most terpenoids) has reached 130 g/l in an engineered S. cerevisiae, which can be used as a platform strain for isoprenoid production. Yarrowia lipolytica is also a suitable chassis cell for steroid and terpenoid production owing to its highly active TCA cycle and better hydrophobic chemical transport capacity. The highest titers of campesterol and carotenoids have been achieved in Y. lipolytica. Aromatic chemicals are mainly biosynthesized from the shikimate pathway and have many industrial applications, such as nutraceuticals, pharmaceuticals, and the building blocks of materials. Compared with other microorganisms, Corynebacterium glutamicum exhibits higher resistance to aromatic chemicals, and the highest titers of shikimate (141 g/l) and 4-HBA have been achieved in C. glutamicum [35]. PKs and NRPs are usually produced by bacteria, fungi, and plants and are biosynthesized by PK and NRP synthases. Streptomyces is commonly used as a chassis cell for the discovery and production of microbe-based PKs and NPRs. Y. lipolytica may be the ideal host for the synthesis of type III PKs because the highest titers of triacetic acid lactone have been achieved in Y. lipolytica.

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Figure I. Natural Products Synthesized in Microorganisms. Typical classes of natural products biosynthesized in nonconventional chassis cells. The advantages and disadvantages of classic and nonconventional chassis cells. 6-Deoxyerythronolide B is the precursor of erythromycin. Abbreviations: Bsu, Bacillus subtilis; Cgl, Corynebacterium glutamicum; Eco, Escherichia coli; GRAS, generally regarded as safe; Sce, Saccharomyces cerevisiae; Strep, Streptomyces; Yli, Yarrowia lipolytica; NR, no reports. See [17,22,23,31,34,36,40,41,52,67,97–132].

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the expression of each gene in the gene cluster. Second, the Kcat values of secondary metabolism enzymes are generally lower than those in primary metabolism [52]. Therefore, it will be necessary to find ways to increase their performance in heterologous hosts. Third, as cellular resources are limited in the cell, forcing too much flux towards the product of choice could interfere with other functions of the cells, impairing fitness. Thus, finding the right balance between growth and production is essential. Recently, metabolic engineering based on novel DNA assembly strategies, genome editing tools, protein engineering, and genetic circuits have been developed to reconstitute, integrate, and

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Figure 2. Biosensor-Based Strategies and Genome Editing Tool Implementation in Chassis Cells. (A) Quorum sensing (QS) and a myo-inositol biosensor were used for layered control of cell growth and glucaric acid synthesis. Abbreviations: AHL, acyl homoserine lactone; EsaI, AHL synthase; EasR, transcriptional activator; ispA, transcriptional repressor; MI, myo-inositol; mioX, myo-inositol oxygenase; pfkA, 6-phosphofructokinase isozyme1. (B) A high-fidelity module and mutagenic module were constructed to control the mutation rate of the host cell. (C) A pyruvate carboxylase [pyc; essential for the production of tricarboxylic acid cycle (TCA) cycle-derived metabolites] mutation library was developed using error-prone PCR. Meanwhile, a lysine biosensor was used to detect the concentration of lysine and report as egfp (GFP). The high-yield strain was selected by fluorescence-activated cell sorting. (D) Using yeast organelles to improve the titer of natural products. (E) Using artificial scaffolding to cluster multistep enzymes. (F) Direct fusion of two correlative enzymes. (G) Nuclease-deficient Cas9 (dCas9) was linked to a transcriptional activation or repression domain to achieve regulation of gene expression. Cas9 was used to delete and insert target genes. (H) dCas9 was linked to cytidine deaminase to catalyze base substitution (C → T). (I) dCas9 was linked to a transposon for gene insertion.

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optimize the metabolic networks and shorten the build and test cycles required to construct the chassis cells (Figure 2). Module Reconstitution Based on DNA Assembly Technology Recently, several novel DNA assembly tools have been developed to facilitate the assembly of large-sized and multicomponent synthesis modules (Table 2). These tools can be divided into

Table 2. Experimental Strategies in the Construction of Chassis Cells for Natural Products DNA assembly Golden Gate assembly

Scarless method that can assemble multiple DNA fragments into specific plasmids using type IIs restriction enzymes

[53]

Start-Stop assembly

Optimized Golden Gate assembly method, which can assemble 60 DNA parts in one destination vector

[54]

One-step SLIC

Scarless and one-step method for DNA assembly based on 3′-to-5′ exonuclease activity of T4 DNA polymerase

[145]

Gibson assembly

Scarless, one-step, and isothermal DNA assembly method, which can assemble multiple DNA fragments into any plasmid using T5 exonuclease, Taq DNA ligase, and Pfu DNA polymerase

[55]

TEDA

Optimized Gibson assembly method, which requires only T5 exonuclease; thus, costs were significantly reduced (~1200-fold)

[146]

LCR assembly

DNA assembly method that can assemble 20 DNA fragments in one step by introducing single-stranded bridging oligos between two neighboring DNA parts

[147]

TAR

Large DNA fragment capture and cloning method depending on the highly efficient homologous recombination system of Saccharomyces cerevisiae

[56]

CATCH

One-step targeted clone method, which can capture 100-kb DNA genomic sequences based on Cas9 and Gibson

[148]

Programmable genome engineering

Genome assembly method, which can rearrange 1.55-Mb genome sequences by combining Cas9 and lambda-red recombination

[59]

MAGE

Simultaneous editing of multiple genes using short single-stranded oligonucleotides (ssDNA); capable of simultaneously targeting multiple genes with moderate efficiency but has extensive off-target mutagenesis and low portability

[60]

TALLEN

Simultaneous editing of multiple genes using TALENs; has high portability and moderate off-target effects but low multiplex ability

[61]

CRISPR/Cas

Genomic DNA is specifically cleaved under the guidance of RNA; has a simpler manipulation process and higher efficiency

[62]

CRISPRi

Represses the transcription of a gene using guide RNA and inactive Cas

[63]

CRISPR-AID

Trifunctional system that can simultaneously achieve gene deletion, transcriptional activation, and repression

[64]

Two enzymes linked using small peptide

[70]

Genome editing tools

Protein engineering Direct fusion Artificial scaffold

Uses protein/DNA/RNA scaffold and affinity tags to recruit enzymes

[69]

Subcellular compartmentalization

Introduction of metabolic pathways into appropriate organelles to increase precursor concentrations and reduce leakage of toxic metabolites

[72]

Dynamic regulation

Using biosensors to respond to the concentration of intracellular metabolites, thus activating or inhibiting the expression of related genes

[74]

Directed evolution

Using biosensors to respond to the concentration of intracellular metabolites, thus controlling the expression of high-fidelity and mutagenic modules

[76]

High-throughput screening

Genetic circuit links biosensors to reporter genes, thereby accelerating the process of strain screening

[77]

Genetic circuits

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two classes: in vitro and in vivo. The most commonly used in vitro assembly tools are Golden Gate and Gibson. Golden Gate assembly is based on type IIs restriction enzymes, which can achieve multicomponent and combinatorial assembly by building modular libraries and without leaving ‘scar’ sequence [53,54]. However, it is still limited by DNA sequence due to its dependence on restriction enzymes. Alternatively, Gibson assembly can also achieve multicomponent assembly, and specific vectors and sequences are not required [55]. However, the efficiency of Gibson assembly decreases significantly when the DNA parts are more than four and combinatorial assembly of module parts is difficult. Compared with in vitro assembly, in vivo assembly methods are capable of assembling larger DNA fragments but require longer homologous arms. Transformation-associated recombination (TAR) is widely used for the assembly and heterologous expression of gene clusters by using the highly efficient homologous recombination system of S. cerevisiae [56]. It can efficiently obtain large DNA fragments (~300 kb) from the genome without introducing mutations but requires suitable restriction sites. Recently, a CRISPR system was used to assist the assembly of large fragments due to its better targeting specificity and versatility [57]. By combining CRISPR with a Golden Gate, Gibson, or TAR system, assembly efficiency and the length of the DNA fragments have been significantly improved (1.55 Mb) [58,59]. In conclusion, it is necessary to select a suitable DNA assembly strategy to reconstitute the heterogeneous synthetic module of natural products. If only a few synthetic modules need to be assembled, Gibson is a preferred assembly strategy. Conversely, if a large number of the synthetic modules need to be assembled, the modular libraries have been constructed in the laboratory, and combinatorial assembly is required, Golden Gate is a more suitable assembly strategy. Finally, if the synthetically relevant genes are clustered and large (e.g., PKs, N30 kb), in vivo assembly methods are more advisable strategies. Precision Genome Editing Subsequently, the assembled modules need to be introduced into the chassis cells. To accelerate the strain-building stage, genome editing tools with multiplexing capacity – the ability to edit multiple targets simultaneously – have been developed (Table 2). The previous generation of genome editing tools included multiplex automated genome engineering (MAGE) and transcription activator-like effector nucleases (TALENs) [60,61]. MAGE could simultaneously modulate the expression level of 20 genes to generate 4.3 billion combinatorial genomic variants per day and successfully increased the production of lycopene fivefold within 3 days [60]. The new generation of genome editing tools are based on CRISPR due to its simpler manipulation process and higher efficiency. The original engineered CRISPR systems had the function of knocking out and inserting genes [62]. Later, the use of nucleasedeficient Cas9 (dCas9) enabled the creation of transcriptional activation (CRISPRa) and interference (CRISPRi) functions, by fusing transcriptional activation and inhibitory domains [63]. Moreover, trifunctional systems were developed by coupling these three systems, which can activate transcription, repress transcription, and delete genes simultaneously [64]. This system was used in the mevalonate pathway to simultaneously overexpress the rate-limiting gene HMG1, repress the competing gene ERG9, and delete the transcriptional regulator ROX1; the production of β-carotene was increased threefold [64]. However, the deletion and integration rate greatly depend on the homologous recombination or nonhomologous end-joining efficiency, which varies between different cell types. Later, CRISPR-based genome base editing tools were developed by using dCas9 to recruit cytidine or adenine deaminases [65]. Recently, a transposon-encoded CRISPR 10

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system with the ability to insert genes into genomes depending on the transposon Tn7 has been developed [66]. Such genome editing tools are the methods of choice to engineer bioproduction. Protein Engineering-Based Enzyme Improvement Several synthetic biology tools have been constructed to modulate the expression levels of enzymes, such as promoter and RBS libraries, repetitive extragenic palindromic (REP) sequences, and N-terminal coding sequences (NCSs) [67]. However, these strategies, based on increasing the number of enzymes, require an elevated number of cellular resources. In particular, the biosynthetic pathways for natural products usually contain multiple steps and overexpression of all of these enzymes would impair cell growth. Therefore, an ideal alternative is to increase the Kcat of the enzymes. For example, by optimizing precursor supply and optimizing the Kcat of the rate-limited enzymes geranylgeranyl diphosphate synthase (GGPPS) and levopimaradiene synthase (LPS), the titer of levopimaradiene increased 2600-fold [68]. Artificial scaffolding is another efficient tool to enhance enzymatic activities, which could create substrate channels and enzyme clustering and may increase the titer of the natural product of choice. For example, a 77-fold increase of mevalonate titer was achieved by using a protein scaffold to cluster three enzymes in the MVA pathway [69]. In addition, the direct fusion of proteins could also increase their catalytic efficiency. For instance, the efficiency of fusing a P450-type protopanaxadiol synthase (PPDS) and a NADPH-cytochrome P450 reductase (ATR1) increased by 4.5 times, which increased the titer of protopanaxadiol by 71.1% [70]. In eukaryotes, enzymes are often located in specific organelles to improve catalytic efficiency and prevent the diffusion of toxic metabolites into the cytoplasm, such as in the case of P450. The N terminus of such a class of proteins often has a transmembrane domain. In some cases, cleavage of the N terminus is necessary for enzyme maturation. By truncating a signal peptide in monoterpene synthase, the titer of linalool was increased 15-fold [71]. By contrast, compartmentalizing the metabolic pathway into a suitable organelle could improve the titer of natural products. For example, guiding sesquiterpene synthases to the mitochondria could increase the titer of valencene and amorphadiene about threefold and sevenfold, respectively [72]. Genetic Circuit-Based Metabolic Pathway Optimization Genetic circuits can be programmed to automatically control the metabolic pathways in response to ligands such as intermediate metabolites, environmental parameters, or signal molecules [73]. Therefore, they are widely used in the dynamic regulation, directed evolution, and highthroughput screening of microbial cell factories. Dynamic regulation, at the forefront of synthetic biology, enables automated distribution of the metabolic flux between biomass and product formation; this approach can be used to alleviate the accumulation of toxic intermediates, which is a major challenge in the synthesis of natural products [74]. Dynamic regulation requires a metabolite-responsive biosensor to detect the input changes (the concentration of metabolite) and deliver the output signals (control enzyme expression). For example, a farnesyl pyrophosphate (FPP) biosensor has been used to regulate the metabolic flux of the MVA pathway and alleviate the accumulation of the toxic intermediate FPP. This strategy increased the titer of amorphadiene approximately twofold over the use of constitutive promoters only [75]. By coupling these genetic circuits to genes that are essential for cell growth, genes that control mutation rates, or fluorescent reporter genes, they can be used for directed evolution and highthroughput screening of strains, which are effective ways to improve desired traits. This is especially relevant when the genetic background of the strains is unclear and/or the synthesis pathway

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of products is complex or poorly understood. For example, a genetic circuit based on a naringenin biosensor has been designed to control the expression of a gene necessary for survival, pushing the strain to produce more naringenin and alleviate the selective pressure, which resulted in a 22-fold increase of naringenin titer [76]. High-throughput screening is an efficient way to test the production of large strain libraries, which can be generated by mutation breeding, DNA assembly, genome editing, or directed evolution. Unfortunately, the throughput of most analytical techniques, such as liquid chromatography–mass spectrometry (LC-MS) and gas chromatography–mass spectrometry (GC-MS), is too low and may become the limiting step. A biosensor-based genetic circuit could link the concentration of products to fluorescence, thereby accelerating the screening process. Recently, an ultrahigh-throughput microfluidic screening method based on RNA aptamers and a droplet microfluidic platform have been developed to enhance the production of tyrosine 28-fold [77].

Transporter Engineering Facilitates the Secretion of Natural Products To achieve high titers of the final product, an efficient secretion system is necessary, because intracellular levels are limited to the cellular space and most natural products are toxic, especially

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Figure 3. Strategies for Transporter Identification. (A) Different expression genes were selected from comparative transcriptome data on high-yield and low-yield strains. These genes in the strain will then be knocked out or overexpressed and finally the distribution of the product or the resistance of the strain to the product (for toxic products) is detected. (B) Genome DNA was fragmented into 1–3-kb fragments and cloned into plasmids. Then, these plasmids were co-transformed into a host strain containing a transporter selection system, which is based on a product-responsive biosensor. Finally, these strains were cultured on selective plates to detect which strain harbors the corresponding transporter. Abbreviation: LC-MS, liquid chromatography–mass spectrometry.

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xenobiotics [78]. Many metabolites cannot diffuse passively across the cell membrane, and their transport must be facilitated by proteins [79]. Membrane transporters can excrete substances out of the cell, reducing their toxic effects on cell growth and improving its production. To solve these problems, transporter engineering has recently emerged. Identifying transporters for specific natural products is the first step towards improved secretion (Figure 3). Recently, several useful transport databases that utilize homology analysis to annotate and classify the predicted transport-coding protein from the genomes of all types of organisms have been developed, such as TransportDB, TCDB, and VARIDT [80–82] (Table 1). However, the function of numerous transports cannot be predicted by homology analysis, because the relationship between the DNA sequence, structure, and function is not obvious, the substrate selectivity of many transporters is highly variable, and it is difficult to obtain crystal structures for the membrane proteins. Therefore, laborious genome-wide gene deletion strategies are still used to identify transporters [83]. Transcriptome-based expression profiling and genome editing tools can reduce the numbers of candidate genes for test and experiment cycles [84,85]. In addition, overexpression of the candidate transporters is another method for transporter identification [86]. However, the heterologous production of membrane proteins remains a challenge, despite the development of methods to overcome this, such as fusion-tag, bicistronic design (BCD) elements, and toxic inhibitory proteins [87–89]. Nevertheless, besides expressing or knocking out candidate transporters, their activity must be characterized by metabolic flux analysis and LC-MS, which often limits high-throughput screening. Recently, ligand-responsive biosensors were used to assist the high-throughput screening of novel transporters. A selective pressure was created based on a ligand-responsive riboswitch and this pressure could be reduced or eliminated only when the strain expresses a specific ligand transporter [90].

Outstanding Questions How can we use advanced artificial intelligence systems to extract and summarize key information and patterns from existing big data, update existing synthetic biology tools, and guide us to design and construct chassis cells? How can we make full use of existing chassis cells and natural overproducers to synthesize the same types of natural products? How can we efficiently reprogram these natural overproducers? How can we improve the universality of genetic circuits to regulate the complex metabolic networks of different species of strains? How can we build artificial biosensors to meet the needs of regulating different metabolic pathway and/or modules? How can we improve our understanding of membrane proteins to accelerate the development of transporter engineering?

After identifying the transporters and overexpressing them in the chassis cell, it is important to further increase their performance. A commonly used method to improve performance is directed evolution [91]. Mutant libraries using error-prone PCR are widely used to generate transporter variants. These variants can be combined with biosensor-based high-throughput screening strategies to screen for transporters with higher performances [92]. In addition to the overexpression of specific transporters, some studies have attempted to increase secretion by overexpressing native or heterologous multidrug efflux pumps, which have a wide substrate specificity, especially for antibiotics, such as PKs and NRPs [93]. The activity of multidrug efflux pumps can be modified by combining or adjusting the expression levels of their components, and this method has been used to increase isoprenoid transport [94]. Recently, an artificial membrane vesicle trafficking system was constructed to assist the secretion of hydrophobic compounds, which increased the yield of β-carotene by a factor of 3.2 [95]. This is a promising tool to improve the transport of hydrophobic natural products, such as steroids. In conclusion, the whole process of constructing the chassis cell for natural product synthesis can be achieved through the design-build-test cycle. The specific limitation found at each step of the cycle will require the development of experimental designs to overcome them. They may include the finding of new enzymes, pathways, and transporters as well as optimization of the expression of each gene in the pathway to maximize production.

Concluding Remarks and Future Perspectives In conclusion, recent advances in systems and synthetic biology enable the engineering of chassis cells for the synthesis of natural products. The acquisition of big data from genomes, transcriptomes, proteomes, and metabolomes, together with large experimental datasets, provides abundant resources for machine learning or artificial intelligence strategies. Artificial Trends in Biotechnology, Month 2020, Vol. xx, No. xx

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Figure 4. Future Perspectives on the Construction of Chassis Cells for Natural Products. In the future, artificial intelligence should be used to assist in genetic model building and protein and metabolic pathway design. Existing genetic models and databases should be optimized by integration with the existing omics data, thereby improving the accuracy of the models. Strain-independent genome editing tools should be developed to enable efficient gene editing of nonconventional microbes as well as natural overproducers. The construction of standardized regulatory elements will shorten the cycle of complex genetic circuit design. In addition, the design of nonnative regulatory elements (transcription factors and ribozymes) will expand the toolbox for controlling gene expression. Finally, an efficient transporter or membrane protein expression, purification, and structural analysis toolbox should be established to accelerate the identification and modification of transporters.

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intelligence has the potential to assist and guide the prediction and design of pathways and the construction of genetic circuits and regulatory elements, and to perform structural and functional analysis of proteins. As discussed above, host selection is a key aspect for achieving high production. Although many nonconventional microorganisms are being successfully used as chassis cells, the synthetic biology strategies for engineering them are still lagging behind classic chassis cells, especially due to the lack of efficient genetic manipulation tools (see Outstanding Questions). Recently developed CRISPR systems are expected to facilitate the manipulation and use of both nonconventional chassis cells and natural overproducers. It is also crucial to develop strain-independent genome editing tools, such as the CRISPR-transposon system. Currently, dynamically regulated circuits based on biosensors need to be adjusted or even rebuilt when the host is changed. It would be therefore important to develop universal regulatory elements of genetic circuits that are stable across different chassis cells. These regulatory elements should be broad spectrum and unified in their response. Moreover, the repertoire of biosensors for natural products is very limited and needs to be expanded. Artificial transcription factor based-biosensors can be now created by using directed evolution, computational tools, or the de novo design of ligand-binding domains. In the case of riboswitch-based biosensors, RNA aptamers are central components, and these can be selected by using systematic evolution of ligands by exponential enrichment [96]. Finally, transporter identification and engineering could be significantly improved to overcome their many current limitations, which begin with a better understanding of membrane proteins. Standardized methodologies for membrane protein expression, purification, and structural and functional characterization should be developed in the future (Figure 4). Acknowledgments This work was financially supported by the Key Research and Development Program of China (2018YFA0900300 and 2018YFA0900504), the National Natural Science Foundation of China (31930085, 31622001, 31870069, 21676119), the Fundamental Research Funds for the Central Universities (JUSRP51713B), and BBSRC (BB/R01602X/1). Thanks to Xueqin Lv, Nan Li, Zhenmin Liu, Jianghua Li, and Jian Chen for their suggestions on the manuscript.

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