The evolution of new traits in living organisms occurs via the processes of mutation, recombination, genetic drift, and selection. These processes that have resulted in the immense biological diversity on our planet are also being employed in metabolic engineering to optimize enzymes and pathways, create new-to-nature reactions, and synthesize complex natural products in heterologous systems. In this review, we discuss two evolution-aided strategies for metabolic engineering—directed evolution, which improves upon existing genetic templates using the evolutionary process, and combinatorial pathway reconstruction, which brings together genes evolved in different organisms into a single heterologous host. We discuss the general principles of these strategies, describe the technologies involved and the molecular traits they influence, provide examples of their use, and discuss the roadblocks that need to be addressed for their wider adoption. A better understanding of these strategies can provide an impetus to research on gene function discovery and biochemical evolution, which is foundational for improved metabolic engineering. These evolution-aided approaches thus have a substantial potential for improving our understanding of plant metabolism in general, for enhancing the production of plant metabolites, and in sustainable agriculture.
Land plants produce an enormous array of metabolites as unique adaptive strategies to survive in harsh terrestrial environments. Many of these metabolites are involved in plant stress response as well as in plant communication with its environment (Moore et al. 2014; Moghe and Kruse 2018). Plant metabolites have been used for food, industrial raw materials, traditional herbal medicines, and cultural and religious practices for centuries. Of the 1394 small-molecule drugs approved by the United States Food and Drugs Administration and other agencies between 1981 and 2019, 64% were derived or inspired from natural products (Newman and Cragg 2020). Overall, at least 122 plant-derived compounds are used as drugs in their purest form (Fabricant and Farnsworth 2001; Kinghorn et al. 2011), but between 10,000 and 53,000 plant species are estimated to be used in traditional medicine (Soejarto et al. 2005; Gurib-Fakim 2006), revealing the enormous untapped potential of plants as sources of drugs. Unfortunately, despite their importance, extraction of specialized metabolites from plants creates new problems related to environmental sustainability, the balance of supply–demand, and equitable distribution of the extracted products (Reed and Osbourn 2018; Jacobowitz and Weng 2020). Chemical synthesis is frequently challenging and cost-inefficient because of the structural complexity of metabolites (Reed and Osbourn 2018). Thus, approaches for synthetic biology-based production of these specialized metabolites have seen increased interest over the last decade.
The hundreds of thousands of compounds estimated to be produced in plants, including > 40,000 terpenoids, > 20,000 alkaloids, > 8000 polyphenols (Rai et al. 2017; Matsuura et al. 2018; Scossa and Fernie 2020) are a product of the complexity in the underlying metabolic and gene regulatory networks that define the chemical scaffolds and their tissue-specific distribution. This complexity is created via the presence of diverse enzyme families, enzyme promiscuity, and the temporal and spatial expression variability of these enzymes. The lineage-specific evolution of networks thus has created diverse enzymatic activities across the plant kingdom that can be tapped for metabolic engineering. In this review, we discuss two approaches—directed evolution and combinatorial pathway reconstruction—that use the principles of the evolutionary process and/or the genetic diversity created via evolution for the production of new proteins and chemicals. While these artificial evolution approaches are aided by the evolutionary process, they also accelerate the process of change by rapidly creating new biochemical phenotypes and thus also aid evolution. Broadly speaking, these approaches are not novel; however, the genomics and informatics revolutions of the past decade have created new opportunities to utilize them for metabolic engineering and for understanding basic plant biochemistry, which we review here.
Directed evolution: general strategies
One of the most successful approaches for metabolic engineering is directed evolution, which mimics the Darwinian evolution process in more controlled, efficient, rapid, and rational ways. Evolution is essentially a random process—mutations such as single nucleotide polymorphisms and indels are created in the genome more or less randomly during DNA replication and/or due to DNA damage/repair. These mutations produce the standing natural variation in different populations of a given species, most of which are neutral or nearly neutral (Kimura 1983). Only in some cases do these mutations affect the phenotype. Depending on the population size and selection pressure, mutations that positively influence fitness by changing an underlying phenotype get carried over to the next generation, positively selected, and eventually fixed in the species.
Directed evolution adapts this strategy for optimizing the activity of specific enzymes (Chatterjee and Yuan 2006). This involves a mutagenesis method to create mutations in target gene(s) followed by screening, often repeated multiple times to select protein/enzyme variants with superior performance (Fig. 1). Although the overall strategy for directed evolution is straightforward, the process of choosing the right strategy at each step is non-trivial as we discuss below.
The first challenge is identifying the starting point for optimization. The gene to perturb is selected based on the final enzyme whose activity needs optimization. Such an enzyme can be (i) a rate-limiting or allosterically regulated enzyme of a known biosynthetic pathway for the end-product being desired, (ii) a naturally occurring enzyme having a desirable promiscuous activity that needs to be increased, (iii) a broad specificity enzyme resurrected by predicting ancestral states of a set of homologs, (iv) a participant in a transient or stable protein–protein interaction, or (v) computationally designed enzymes e.g. retro-aldolases (Jiang et al. 2008), Kemp eliminase (Röthlisberger et al. 2008) whose activities need to be optimized further. Enzymes that are water-soluble and not too difficult to express in their active forms in ready production systems like E. coli, yeast, N. benthamiana, or other whole-cell systems are preferable.
The next step is choosing the right method for generating genetic diversity. There are three primary means of mutagenesis: site-directed (single or multiple sites), random, and semi-random. The benefit of site-directed mutagenesis e.g. using CRISPR-Cas9 is that it can be grounded in the knowledge of protein structure, catalytic mechanism, and/or evolutionary conservation of active site residues. Interpretation of results can be more straight-forward, and the number of assays to test activity evolution are practically manageable. However, such a strategy is not always optimal for producing large increases in enzyme catalytic efficiency required for industrial-scale optimization. Furthermore, testing various combinations of single mutations may not be practically and economically feasible. Random mutagenesis gets around the hurdle of requiring protein structural knowledge by randomly changing much of the protein sequence. Strategies used include (i) mutagenesis methods such as error-prone PCR, random insertion/deletion mutagenesis, saturation mutagenesis, and phage-assisted continuous evolution (PACE) as well as (ii) homologous recombination-based methods including DNA shuffling, multiplex automated genome engineering (MAGE), and saturated targeted endogenous mutagenesis editors (STEMEs) (Stemmer 1994; Wang et al. 2009; Esvelt et al. 2011; Engqvist and Rabe 2019; Gionfriddo et al. 2019; Li et al. 2020b) (Fig. 1). STEMEs, for example, were developed to generate diverse in-frame mutations in plants by employing CRISPR-based cytosine or adenine base editors using only one single-guide RNA. The random nature of the evolutionary process can be made slightly less random by performing saturation mutagenesis in a specific region of the enzyme (Acevedo et al. 2017), also termed semi-random mutagenesis. This approach attempts to address the problem of low productive to non-productive mutants ratio in random mutagenesis, also reducing the number of mutants to screen. While the knowledge of protein structure can help in semi-random mutagenesis, new machine learning (ML) methods—discussed in more detail later—are also being developed to identify sites and regions to mutate.
A directed evolution experiment with random mutagenesis can produce hundreds to hundreds of thousands of gene variants. As a result, developing an optimal screening method for these variants presents another set of challenges. Different methods have their own limitations that need to be carefully considered for experimental design. For example, directed evolution to improve protein stability may simply require a system such as split-GFP (Cabantous et al. 2005) to detect the degree of solubility. Colorimetric, fluorometric or luminometric assays are available for some compounds (e.g. carotenoids, anthocyanins) and for direct/indirect detection of several enzyme products, but they are not necessarily high-throughput, cost-effective, or free of noise from background reactions catalyzed by enzymes from the host. Mass spectrometry, especially direct injection systems e.g. Agilent RapidFire™ and flow-injection mass spectrometry can enable screening for products in seconds per reaction, allowing for ultra-high throughput screening (uHTS). The downsides with mass spectrometry are the high initial setup cost running into hundreds of thousands to over a million dollars, high technical expertise for regular maintenance, and a limited ability to elucidate completely novel structures. Fluorescent Assisted Cell Sorting (FACS) is also an attractive method, enabling uHTS of > 107 variants per hour (Tan et al. 2019). FACS may require the development of appropriate fluorescent substrates that would be retained in the cell post-reaction, for which varied strategies have been devised (Markel et al. 2020). To further address this constraint, non-cellular in vitro compartments such as emulsions, hydrogel beads, microwells, polymerosomes that can retain the product have also been developed for uHTS (Markel et al. 2020). Several uHTS methods such as protein-based biosensors, emulsion-droplet-based mass spectrometry, surface display, antibiotic-based bioassays have been reviewed before (Xiao et al. 2015; Markel et al. 2020), and we direct the reader to these excellent resources. Where utilization of such uHTS measures is not available, a prior step of rational library selection using structural or evolutionary information or using ML approaches is recommended.
Finally, iterative mutagenesis and screening experiments may lead to the identification of multiple variants with improved activities. Enzyme regions modified in the first iteration can be recombined with each other to produce better performing enzymes. One strategy used here (DNA shuffling) involves mixing the variants in a single tube, shearing them, and performing ligation and PCR without adding external primers (Stemmer 1994). Advances in gene synthesis technologies that make it more affordable (e.g. gBLOCKS™) also allow mix-and-match synthesis of different gene regions producing recombinant enzymes.
Examples of enzyme traits evolved by directed evolution
The directed evolution approach has been successfully used for enhancing properties of proteins/enzymes such as thermostability, enantioselectivity, substrate specificity, solubility, and photophysical properties without prior knowledge of protein structural information (Arnold 2018). A classic example of directed evolution of individual proteins—which led to the 2018 Nobel Prize in Chemistry—is the development of a superior enzyme variant of Escherichia coli subtilisin E, a serine protease widely used for enzyme catalysis in commercial products and synthetic organic chemistry (Chen and Arnold 1993). The applications have been more varied since then, however, most of the experiments are performed using microbial or in vitro systems rather than plants due to the practical issue of their long generation times vs. bacteria and yeast. Recently, a new system for CRISPR-Cas-directed evolution was designed using rice as a model (Butt et al. 2019). The authors demonstrated saturation mutagenesis of the rice SPLICING FACTOR 3B SUBUNIT 1 (OsSF3B1) in planta using a library of 119 guide RNAs. Using resistance to the splicing inhibitor herboxidiene as a selectable trait, resistant OsSF3B1 variants were selected through rice seedlings surviving on culture plates, and these variants were further optimized through targeted engineering of a domain known to be involved in herboxidiene interactions (Butt et al. 2019). While this study demonstrates the feasibility of in planta directed protein evolution for some traits, new genome-wide strategies using multiplexed CRISPR may enable similar directed evolution of entire metabolic pathways. While not in plants, MAGE, which can induce simultaneous modification at many genomic locations, was used to optimize the biosynthesis of 1-deoxy-d-xylulose-5-phosphate (DXP) in E. coli to enhance the production of commercially important isoprenoid lycopene. The simultaneous modification of 24 endogenous genes involved in DXP pathway was carried out to increase the metabolic flux through the pathway, enhancing the lycopene yield by fivefold (Wang et al. 2009). Multiple combinatorial strategies have also been used to improve the production of compounds such as flavonoids, carotenoids, and pharmaceutical compounds with complex structures that are not conducive to sustainable natural production or economic artificial synthesis (Schmidt-Dannert et al. 2000; Wang et al. 2000; Pandey et al. 2016; Li et al. 2017; Hong et al. 2019; Sheng et al. 2020).
The most common application of directed evolution is to improve an enzyme’s interaction with its substrate under specific conditions. For example, directed evolution was successfully used to develop several Cas protein sequence variants with improved fidelity for CRISPR–Cas9-mediated genome editing (Casini et al. 2018; Lee et al. 2018; Goldberg et al. 2021). Goldberg et al. (2021) identified individual sequence variants—many in the active site—which when combined into a quadruple mutation, had a significantly greater discriminative capacity at protospacer adjacent motifs required for Cas9 cleavage. PAM-less genome editing was achieved in human cells and plants by an engineered Cas9 variant developed through structure-based directed evolution (Walton et al. 2020; Ren et al. 2021), wherein substitutions of previously known active site residues were selected via a high-throughput screening approach (Walton et al. 2020). In another distinct example with agricultural applications, the catalytic performance of a Brassica napus diacylglycerol acyltransferase 1 (DGAT1) for the production of triacylglycerol, a predominant component of vegetable oil, was improved using yeast as a screening host and Nicotiana benthamiana as a production host (Chen et al. 2017). 67% of the 81 residues that increased activities were conserved among plant DGATs. These studies highlight that residues in the active site, residues interacting with the substrates, or highly conserved residues may potentially be used as starting points for a semi-random mutagenesis approach in order to alter enzyme–substrate interaction dynamics. As the examples below also demonstrate, the largest increases are seen through additive or synergistic interactions between multiple mutations.
At a higher level than the primary sequence, protein structure is an important trait of interest for engineering. One of the first examples of directed evolution involved altering the E. coli beta-glucuronidase (GUS) variants, critical for plant gene expression studies, for resistance to glutaraldehyde and formaldehyde used in tissue fixation (Matsumura et al. 1999) and for thermostability (Flores and Ellington 2002; Xiong et al. 2011). Matsumura et al. (1999) found that a majority of the changes that increased glutaraldehyde resistance ~ 80-fold occurred on the protein surface, possibly influencing lysine cross-linking and quaternary structure formation. Another high-value engineering target is RuBisCo, which has a very low catalytic rate and is prone to performing oxygenation, minimizing net carbon fixation. Low throughput photosynthetic selection systems (e.g. Rhodobacter capsulatus) and high throughput RuBisCo-dependent E. coli selection systems have been developed for screening mutagenized variants (Wilson et al. 2016, 2018). Approaches such as evolving RuBisCo variants or ancestrally reconstructed RuBisCo sequences are being explored (Shih et al. 2016a) to improve the enzyme’s catalytic properties. However, another approach involves targeting the enzyme’s folding properties. The “green” type RuBisCo from vascular plants requires over seven different chaperones for proper folding and assembly, a trait which is being evolved using templates from “red” type RuBisCo variants from purple bacteria, which do not have extensive chaperone requirements (Gunn et al. 2020). Such easily foldable RuBisCos would be more amenable to further evolve and improve in microbial systems.
Conformational diversity is an important aspect of protein structure that is not only important for structural stability under non-native conditions but also for improved reaction properties. An interesting example in this context is a computationally designed enzyme called Kemp eliminase HG3 (Röthlisberger et al. 2008), which performs a naturally unseen kemp elimination reaction. During the process of further optimizing this enzyme using directed evolution, 17 mutations were fixed in the protein sequence, of which 11 were close to the active site (Otten et al. 2020). These mutations increased the specific activity of an already good enzyme by > 200-fold. Comparison of room temperature X-ray crystal structures of the parent and the intermediate mutant enzymes revealed that this massive shift in performance occurred due to stabilization of one of the conformational states of the enzyme, leading to the active site being pre-organized for substrate binding and catalysis (Broom et al. 2020). Conformational stability and solubility improvements were also implicated in enhancing the activity of heterologously expressed Type III polyketide synthase involved in tropane alkaloid biosynthesis in Atropa belladonna (Wrenbeck et al. 2019), an important source of pharmaceutically important tropane alkaloids. The stability of protein structural fluctuations was also revealed as the cause of increased thermal stability in the evolved variant of the cellobiohydrolase enzyme from the fungus Hypocrea jecorina (Goedegebuur et al. 2017), used in enzyme mixtures for biofuel production. These examples highlight protein conformational diversity as an engineering target for improving various catalytic parameters. While nuclear magnetic resonance and small-angle X-ray scattering are experimental approaches that can be used to identify protein conformers, computational tools e.g. DynaMut (Rodrigues et al. 2018), SDM (Pandurangan et al. 2017) also enable prediction of structural stability upon mutations.
Since many proteins function via interacting with other proteins, optimizing their interactions can further improve the phenotype of significance. For example, directed evolution was used for increasing the potency of Bt toxins e.g. the toxin Cry1Ab, which is not effective against Nilaparvata lugens (rice brown planthopper) by engineering its receptor-binding domain (Shao et al. 2016). Biopanning, an affinity-based peptide screening method, was used to select mutagenized variants of the N. lugens gut membrane-binding peptides using a peptide-displayed phage library, and the best-performing variant was engineered into Cry1Ab. Protein interaction improvement can also lead to an improvement in substrate channeling—e.g. as demonstrated in the well-studied dhurrin and tryptophan synthase metabolons (Miles et al. 1999; Laursen et al. 2016; Zhang and Fernie 2021)—or simply improve access to local substrate pools. Co-localization of functionally related enzymes by artificial scaffolding using nano-particles (Jia et al. 2013) or by protein encapsulation (Choudhary et al. 2012) have been explored, among other strategies (Pröschel et al. 2015). While such artificial co-localization increases access to substrates, given the high rate of substrate diffusion, efficient channeling of substrates in functionally related enzymes may require those enzymes to be co-evolved or optimized for channeling (Sweetlove and Fernie 2018). Residues that improve electrostatic guiding through the substrate channel or surface residues that improve protein interactions can thus be targets for enzyme engineering. Characterizing binary enzyme-enzyme interaction networks in model plant species would be an important step in that regard (Zhang et al. 2017; Wierbowski et al. 2020).
These examples not only show the breadth of applications of the directed evolution approach but also highlight techniques that can be used for rational engineering of enzyme activities vs. random mutagenesis. Residue conservation, evolutionary coupling analysis, molecular dynamic simulations can reveal residues of significance to enzyme structure and function. Recent methods employing statistical modeling and machine learning e.g. TLmutation (Shamsi et al. 2020), Envision (Gray et al. 2018), EVmutation (Hopf et al. 2017) also seek to identify the impacts of mutations in silico. Saturation mutagenesis or knowledge-based editing of these residues of significance can streamline the process of improving enzyme activities without screening hundreds of thousands of variants.
Combinatorial pathway reconstruction for making novel products
The evolution of genomes in independent lineages has created a rich pool of enzyme “parts” that can be assembled into new synthetic pathways. The combinatorial pathway reconstruction (combinatorial engineering) approach relies on the engineering of multiple transgenes to modify or potentiate existing metabolic pathways. In addition, the combinatorial approach also often aims to build novel pathways that do not naturally exist in the selected host (Fig. 2), thus aiding the evolution of new metabolic phenotypes in the process.
The most popular combinatorial reconstruction strategy is to engineer plant pathways in microorganisms due to their fast growth, high product yields, ease of genetic engineering, and lower complexity of regulatory networks compared to plants (Zhu et al. 2017). In light of these differences, after the initial interest in engineering plants for drug production, the pharmaceutical industry shifted to favoring the engineering of microorganisms for various reasons (Fischer and Buyel 2020). A recent review (Cravens et al. 2019) highlighted how decades of microorganism-based engineering have brought the combinatorial power of metabolic engineering from the co-expression of few genes in the early 2000s to 24 genes in recent years (Li et al. 2018). In the next section, we report increasing complexity levels of combinatorial engineering in microorganisms and plants. A compilation of various technologies and examples of engineering are provided in Tables 1 and 2, respectively.
Combinatorial pathway reconstruction in micro-organisms
Combinatorial engineering is ideally suited for generating operon-like constructs in microorganisms that mimic the naturally occurring gene clusters in many plant genomes (Jirschitzka et al. 2013). An example is the naturally occurring ten gene cluster in the genome of Papaver somniferum involved in the biosynthesis of noscapine, a structurally complex alkaloid (Winzer et al. 2012). Genes involved in the biosynthesis of triterpenoids involved in plant defense responses (Sawai and Saito 2011) have also been described as clusters in the genomes of oat (Avena sativa) and Arabidopsis thaliana (Qi et al. 2004; Field and Osbourn 2008). The biosynthesis of triterpenoids from Medicago truncatula was combinatorially engineered in S. cerevisiae. Two different combinations of β-amyrin synthase, cytochrome P450 reductase (CPR), and different combinations of candidates of the cytochrome P450 (CYP) gene family were used to construct two strains of yeast each producing different end products (soyasapogenol B and gypsogenic acid) (Fukushima et al. 2013). Interestingly, the combination of CYP genes that do not normally work together in planta led to the formation of rare triterpenoids (e.g. 4-epi-hederagenin) not naturally present in M. truncatula. This demonstrates the power of combinatorial engineering for the production of novel molecules outside conventional biosynthetic pathways.
Another way to perform combinatorial engineering is using a limited pool of genes as a toolkit to create different variations of the same metabolic pathway. The production of various phenylpropanoids was engineered in S. cerevisiae using this approach (Trantas et al. 2009). The construction of the yeast strains was based on four pESC vectors, each containing an extensive polylinker sequence and a different selectable marker, allowing the incorporation of up to two genes per vector. This toolkit was used to engineer seven different strains expressing either (i) phenylalanine ammonia-lyase, (ii) cinnamic acid 4-hydroxylase, or (iii) various combinations of CPR, 4-coumaric acid: CoA ligase, chalcone synthase, chalcone isomerase, isoflavone synthase, flavanone-3 hydroxylase, flavonol synthase, and flavonoid-3′ hydroxylase—derived from either soybean, poplar or potato. The resulting end products were p-coumaric acid, trans-resveratrol, naringenin, genistein, kaempferol, and quercetin—all molecules of high value for nutritional and agricultural purposes. This study is a classic example of how synthetic biology applies modern “modular” engineering principles to biological systems (Roell and Zurbriggen 2020).
In recent years, combinatorial engineering has reached unprecedented levels of complexity. A good example is the engineered biosynthesis of (S)-reticuline from the tetrahydroisoquinoline (THIQ) pathway naturally occurring in poppy species such as Papaver somniferum and Eschscholzia californica (California poppy). The THIQ moiety is described in more than 3000 bioactive alkaloids (Pyne et al. 2020). The opioids, morphine, and codeine, are only two of the many THIQ-alkaloids that are extremely valuable for the pharmaceutical industry. Benzylisoquinoline alkaloids are derived from THIQ alkaloids and (S)-reticuline is a central intermediate for benzylisoquinoline alkaloid biosynthesis. By a complex series of over eighty genetic modifications, including deletions of endogenous genes of S. cerevisiae and insertions of genes from P. somniferum, a staggering 57,000-fold increase—from 80.6 µg/L to up to 4.6 g/L—in (S)-reticuline production was obtained compared to the first generation of yeast strain (Deloache et al. 2015). The necessary DNA integrations were achieved using CRISPR/Cas9 along with in vivo DNA assembly using pCas-G418 or hygromycin-resistance derivative (pCas-Hyg) vectors. This is one of the best examples of high product yield coming from combinatorial pathway manipulation in microorganisms.
Another example of the complex genetic design used in combinatorial metabolic engineering of yeast is the reconstruction of noscapine biosynthesis via coexpression of over thirty enzymes, 25 of which are derived from diverse organisms including bacteria, mammals, and plants (Li et al. 2018). This approach resulted in an 18,000 fold increase in noscapine production compared to the initially engineered noscapine synthesizing strain (Li et al. 2018). This achievement required not just the coexpression of multiple enzymes, but also a sophisticated scheme of changes to the base metabolism of S. cerevisiae and a careful choice of growth and fermentation conditions. The final strain produced up to 2.2 mg/L of noscapine, an increase from 120 ng/L.
Combinatorial pathway reconstruction in plants
Historically, a disadvantage of multi-gene transfer in plants was the introduction of unlinked transgenes that would segregate and therefore requires a program of backcrossing to reach homozygosity. Conventional gene stacking, retransformation, and unlinked transgenes are all part of this category (Farré et al. 2014). New vectors like the high capacity binary bacterial artificial chromosome or the transformation-competent artificial chromosome, can deliver very long sequences (> 100 Kb) and allow the integration in the genome of many linked transgenes that do not segregate and do not require any backcrossing (Farré et al. 2014). These kinds of constructs are extensively used in synthetic biology and constitute an essential tool for combinatorial bioengineering in plants. Such new technologies have mostly solved the bottleneck of multi-gene transfer in plants. Nevertheless, decades before this technological leap, it was possible to perform combinatorial pathway reconstruction in plants using more conventional transgenesis methods. One of the first and most famous examples is Golden Rice, first engineered in 2000 to produce high quantities of Vitamin A (β-carotene) accumulating in the endosperm through the expression of two transgenes: phytoene synthase from daffodil and a bacterial carotene desaturase (Ye 2000). Further rounds of improvement led to new versions of Golden Rice with even higher contents of vitamin A (Datta et al. 2003; Hoa et al. 2003; Paine et al. 2005) and lower rates of carotenoid degradation during the storage phase through downregulation of the endogenous lipoxygenase r9-LOX1 (Gayen et al. 2015). Golden Rice is an example of how combinatorial engineering can be achieved by a continuous cycle of design, construction, and testing (Fig. 2). This cycle resulted in an enhanced crop that opens the opportunity to address the serious problem of vitamin A deficiency that affects millions of people around the globe (Paine et al. 2005).
The development of Golden Rice required decades of effort for unlinked integration of multiple genes—a problem now solved by newer multi-gene transfer methods. An example of this kind of approach is Purple Endosperm Rice. This is a transgenic rice strain in which multiple genes have been engineered to produce and accumulate anthocyanins in the endosperm (Zhu et al. 2017). To achieve the desired purple endosperm, six genes from Coleus (Solenostemon scutellarioides) involved in anthocyanin biosynthesis were integrated with two transcription factors from maize: Zea mays Leaf color (ZmLc) and Zea mays Purple leaf (ZmPl) known to activate this biosynthetic pathway. In total, eight different transgenes from orthologous systems driven by maize endosperm specific promoters were engineered into Oryza sativa (subsp. japonica and indica) using a new kind of vector system: TransGene Stacking II in which the eight genes were linked and delivered within a sequence of ~ 31 Kb. The engineering of orthologs from Coleus along with transcription factors from maize allowed to escape the endogenous repression of anthocyanin production typical of rice endosperm. In this way, another large bottleneck of metabolic engineering in plants was addressed: the presence of strong regulatory networks that can inactivate the engineered pathway(s). These achievements demonstrate that the endosperm of cereals is a valuable system for the implementation of combinatorial reconstruction or potentiation of diverse metabolic pathways in staple crops that can address global health-related problems.
While the combinatorial biofortification of crops normally relies on stable transformation, the pharmaceutical industry has used mostly transient expression for the fast and scalable production of valuable compounds, including vaccines, in plants. N. benthamiana emerged as the favored plant system for this kind of engineering (Bally et al. 2018). The combinatorial engineering of metabolic pathways in N. benthamiana may require the co-expression of a low number of transgenes (2–3 in a lower complexity case). In this scenario, single genes can be cloned in separate plasmids and co-expressed through simple co-infiltration with multiple Agrobacterium strains, each carrying only one transgene. This method was successfully used to transiently co-express at least three genes in N. benthamiana for engineering the synthesis of costunolide, a sesquiterpene lactone (Liu et al. 2011). Later, a combinatorial approach involving four genes was used to engineer tropane alkaloid biosynthesis to yield tropinone in N. benthamiana using transient expression (Bedewitz et al. 2018). Tropane alkaloid biosynthesis is naturally observed in many Solanaceae species but not in N. benthamiana. The simultaneous expression in N. benthamiana of polyketide synthase and tropinone synthase from Atropa belladonna resulted in low production of tropinone. Increments in the production of tropinone were obtained with additional coexpression of a putrescine methyltransferase and a methylputrescine oxidase. This is not only an example of modular transient expression but also a case of optimization of plant-to-plant metabolic engineering of an ortholog pathway. Furthermore, other studies report the simultaneous overexpression of N-methylputrescine transferase (PMT) and hyoscyamine 6β-hydroxylase (H6H) from Hyoscyamus niger in Atropa belladonna resulting in a production of scopolamine of up to 1.2 mg/g DW (Wang et al. 2011).
In another study, N. benthamiana was used as the host for the production of taxadiene and taxadiene-5α-ol, precursors of the anticancer drug taxol (Li et al. 2019). This required the construction of a vector carrying eight different genes that were assembled using a modular approach based on Golden Gate cloning system (Engler et al. 2008) and requiring in this case, six different plasmids carrying different gene combinations.
A very high-complexity case of N. benthamiana-based combinatorial engineering is the production of strictosidine, an alkaloid belonging to the group of monoterpenoid-indole alkaloids (MIAs). This is an important precursor of many anticancer and antimalarial drugs that are naturally produced in very low amounts in Catharanthus roseus (Madagascar periwinkle). The limited production makes this drug very expensive, but the whole pathway has been engineered in N. benthamiana via transgene expression of a total of twelve genes. Eight genes are directly involved in the seco-iridoid pathway, two genes to boost the precursors' formation necessary to support the target pathway, and two genes involved in downstream alkaloid biosynthesis (Miettinen et al. 2014). Interestingly, these multi-transgenes were delivered using a well-established vector for transcriptional fusion (pRT101) used as a donor (Töpfer et al. 1987) in combination with Agrobacterium binary vector pCAMBIA1300. These are only a few of the many examples of combinatorial pathway reconstruction in microorganisms and plants. A more comprehensive list of published engineered pathways for several phenylpropanoids, flavonoids, terpenoids, and alkaloids can be found in Table 2. What emerges from the vast available literature on plants and microorganisms is that complex genetic designs of combinatorial reconstruction can be achieved with a very diverse set of tools, with nearly limitless genetic diversity available as parts (modular constructs). Many of these constructs are available through resources such as AddGene (https://www.addgene.org/) and other resources (Table 1). Some of the most popular molecular tools are commercially available and make synthetic biology experiments and applications accessible to an increasing number of researchers worldwide (Choi et al. 2019). A list of available technologies is described in Table 1.
Roadblocks and practical considerations for evolution-aided metabolic engineering
Directed evolution and combinatorial pathway reconstruction are powerful approaches for plant metabolic engineering and are frequently used in tandem. A broader adoption of these strategies requires some hurdles to be addressed. One roadblock is the insufficient knowledge of plant metabolic pathways. For example, complete synthesis of two opiates thebaine and hydrocodone was made possible in yeast because of the discovery of the enzyme reticuline epimerase (Galanie et al. 2015). The elucidation of this pathway was further confounded by reactions such as the epimerization of (S)-reticuline to (R)-reticuline, catalyzed by an unusual cytochrome P450 and aldo–keto reductase fusion enzyme in opium poppy (Papaver somniferum), whose component enzymes are independent entities in other related species (Farrow et al. 2015). Specialized metabolism is replete with such examples of lineage-specific evolution, which can occur even within populations of the same species [e.g. acylsugar and terpene biosynthesis in Solanum habrochaites populations (Gonzales-Vigil et al. 2012; Kim et al. 2012b; Landis et al. 2021)] complicating pathway discovery. Many desirable compounds e.g. drug candidates are identified in non-model plant species for which—unlike reference species like Arabidopsis thaliana and Solanum lycopersicum—appropriate genetic and genomic resources do not exist (Moghe and Kruse 2018). The application of RNA-seq and evolutionary genomics in combination with traditional biochemical methods of obtaining purified enzyme activities is useful in this context (Rajniak et al. 2018; Nett et al. 2020), but there are opportunities for further progress. For example, ML is now increasingly likely to accurately predict protein structure from a sequence (Callaway 2020), and ML is already finding applications in the directed evolution of proteins, to build sequence-function models and select variant libraries for screening (Yang et al. 2019; Shamsi et al. 2020). The use of ML for predicting sequence-function relationships would also be more broadly useful for the functional prediction of genes in the rapidly accumulating but poorly annotated plant genomes (Mahood et al. 2020).
For directed evolution, the requirement of uHTS methods for assaying hundreds of thousands of enzyme variants limits the type of activities that can be optimized using this approach. One solution is the development of smart libraries restricted to specific mutational changes targeting protein traits described above, rather than searching over all of the protein sequence space. Multiple tools are available to better identify residues that may alter function (Sebestova et al. 2014; Wrenbeck et al. 2019) and for making focused libraries (Sayous et al. 2020). In a recent study, ML was used to computationally identify residues that could be modified for a new-to-nature chemical transformation by using a much smaller mutagenesis library (Wu et al. 2019). This particular study was possible because of a large, previously published dataset of ~ 150,000 phenotypes of the mutagenized variants of the target enzyme (Wu et al. 2016), which was used as a training set for ML. Availability of databases such as ProTherm (Nikam et al. 2021) and Meltome Atlas (Jarzab et al. 2020) enable the development of algorithms for protein stability traits. Large publicly available genomics and structure datasets could also potentially be used to narrow down the sequence space for activity optimization.
Combinatorial pathway reconstruction, just like directed evolution, requires making a choice of appropriate starting enzymes, which can be difficult given the vast enzymatic diversity available across life. Combinatorial engineering in heterologous systems may also reveal issues such as end-product inhibition of certain enzymes, unproductive side-reactions, product toxicity, requirements for post-translational modifications, and/or requirements for heteromeric complex formation among others (Fig. 2). Directed (random or targeted) evolution of the concerned enzyme can be useful in this regard. Flux balance analysis can help identify the bottlenecks in a given pathway and optimize metabolite flow. These issues may also warrant a rethinking of the production system used. For example, the use of genetically engineered N. benthamiana was more appropriate for the production of anti-cancer drug taxol through chloroplastic engineering (Li et al. 2019), while carotenoids are better produced in bacterial and fungal systems (Li et al. 2020a). Plant (and mammalian) cell cultures have also been used for the production of many metabolites (reviewed in Santos et al. 2016). In addition, the choice of appropriate vectors for delivery of the heterologous gene(s) and the need for modification of the metabolic networks of the production hosts are important factors to be considered for designing engineering experiments.
Finally, an important consideration is the economics of metabolic engineering. Plants themselves are excellent production systems, and modern agriculture with its vast farmlands can offer scalability beyond the capabilities of microbial fermentation. Furthermore, the inputs required for plants are just light, water and standard fertilizers as opposed to microbes which may require supplementation with substrates and/or antibiotics to avoid contamination. On the flip side, land availability is an important limiting resource for plant-based extraction. Thus, choosing an appropriate production system and optimizing inputs are critically important (Cravens et al. 2019). A recent study (Yang et al. 2020) used a “techno-economic” strategy and calculated the break-even costs of producing compounds such as artemisinin, polyhydroxybutyrate, cannabidiol, limonene etc. using metabolic engineering vs. extraction from plant sources. Such analysis can help prioritize compounds, help design engineering strategies, and determine the sustainability of synthetic biology-based production approaches.
Economics of metabolic engineering also expands into sensitive areas of social acceptance of engineered natural products as well as issues of livelihood of workers that may be rendered jobless as a consequence. A positive example is artemisinin, an antimalarial drug whose pathway engineering can save millions of lives in countries affected by malaria and in turn, be a sustainable alternative to harvesting Artemisia annua. On the other hand, engineering opiates into yeast strains could facilitate illicit home-brewing of drugs such as heroin from sugar, making detection of such supply routes more difficult than tracking poppy fields (Oye et al. 2015). Another contrasting example is saffron—one of the world’s most expensive spices and a crucial source of income for countries like Iran and Afghanistan, with tens of thousands of individuals employed in carefully hand-harvesting stigmas of Crocus sativus flowers (Azimy et al. 2020). Saffron is an important component of the Afghanistan government’s policy to move farmers away from opium production and lifting families out of poverty (The World Bank 2015; Azimy et al. 2020). How would ongoing efforts for synthetic production of carotenoids and aromatics making up the “saffron spice” impact policy and society? More discussion on these issues is needed in the years ahead.
This review highlights two evolution-aided approaches for metabolic engineering, one where existing genetic diversity is used in innovative ways, and another where new diversity is generated to explore beyond the known functional space. It is clear that knowledge of genetic drift, selection, homology gleaned from evolutionary biology is as foundational for designing engineering experiments as the knowledge of chemistry, biochemistry, protein structure, genomics, and molecular biology. Increasingly, ML and computational proteomics are playing important roles in optimizing directed evolution experiments as well as in determining how to combine diverse enzymes into unified pathways. Identifying useful metabolites from the hundreds of thousands produced across plants and discovering pathways for their biosynthesis in native species remain major bottlenecks for metabolic engineering. However, the rapid pace of genome sequencing, functional genomics, and developments in ML have poised the field for rapid innovation. Further improvements in the two discussed evolution-aided methods will continue to come from improved protein sequence-function prediction models and optimization of the individual steps that are part of these approaches.
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This research was enabled by funding to BC, PR, JD from the state of Sachsen-Anhalt and EFRE (Europäischer Fonds für regionale Entwicklung), project HyperSpEED (Hypericum multi-species Exploration of Extracts Diversity)–grant number ZS/2019/07/99749. The project is part of the research cluster Autonomie im Alter (AiA). MI, GM acknowledge funding from the United States Department of Agriculture Hatch Grant number 1021130 that partly enabled this research.
Conflict of interest
The authors declare no conflicts of interest.
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Irfan, M., Chavez, B., Rizzo, P. et al. Evolution-aided engineering of plant specialized metabolism. aBIOTECH (2021). https://doi.org/10.1007/s42994-021-00052-3
- Metabolic engineering
- Plant biotechnology
- Synthetic biology