Synthetic biology to access and expand nature's chemical diversity

Abstract

Bacterial genomes encode the biosynthetic potential to produce hundreds of thousands of complex molecules with diverse applications, from medicine to agriculture and materials. Accessing these natural products promises to reinvigorate drug discovery pipelines and provide novel routes to synthesize complex chemicals. The pathways leading to the production of these molecules often comprise dozens of genes spanning large areas of the genome and are controlled by complex regulatory networks with some of the most interesting molecules being produced by non-model organisms. In this Review, we discuss how advances in synthetic biology--including novel DNA construction technologies, the use of genetic parts for the precise control of expression and for synthetic regulatory circuits--and multiplexed genome engineering can be used to optimize the design and synthesis of pathways that produce natural products.

Figure 1

Natural product biosynthetic gene clusters. (a) Representation of the diversity of size and complexity of NPs and their encoding gene clusters, including tyrvalin, a pyrazinone virulence factor from skin-associated staphylococci, platensimycin, a diterpenoid antibiotic from soil-dwelling Streptomyces isolates, pederin, a polyketide anticancer agent produced by an uncultivated symbiont of the Paederus spp. beetles, and bryostatin, a macrocyclic lactone anticancer agent produced by a symbiont of a marine bryozoan. Approximate sizes of BGCs for select NPs (black), along with noteworthy examples of large systems that have been built with synthetic DNA technology in wild type (red) or re-designed (green) genetic architecture. (b) Widening gap of uncharacterized PKS enzymes (grey) compared to biochemically characterized PKSs (black) since 2000 (data to 2010 reproduced from Wong and Khosla; 2014 data point from Marnix Medema, personal communication). Dashed line represents best fit to available data points. (c) Recent history of DNA synthesis costs and the corresponding number of 50 kb gene clusters that could be synthesized with $100k. Dotted lines project to the future along the same trajectory of the past 15 years.

Figure 2

Genetic refactoring. (a) Schematic outline of refactoring process, and (b) the streamlined refactoring of homologous gene clusters by substituting coding sequences. New homologous cluster and corresponding genetic parts are shown in green, and previously refactored cluster and parts are shown in blue. Bold lines on chemical structures show conserved core scaffold between two enediynes used as a hypothetical example. (c) Refactored epo gene cluster, built into a two plasmid system. Extracted ion chromatogram shows production of epothilones A and B from the refactored gene cluster introduced to M. xanthus (i), but not from the wild-type host (ii).

Figure 3

Genetic parts for controlling gene expression levels. (a) Characterization of genetic parts in E. coli, including (from left to right), promoter variants^,, ribosome insulators, bicistronic RBSs, computationally designed RBSs, and synthetic and natural terminators. (b) Genetic parts for engineering NP-producing organisms, including promoter variants^–, computationally designed RBSs, and codon-optimized CDS parts.

Figure 4

Exploiting refactored genetics for host transfer of multi-gene devices. (a) Schematic representation of a DNA synthesis and assembly pipeline, wherein genetic parts are constructed from synthetic oligonucleotides and then assembled into unique combinations. (b) High-throughput library design of permuted gene clusters for antimalarial phosphonate FR900098. Bar graph shows characterized titers from constructs selected from iterative libraries, with successive libraries from left to right. (c) Experimental design for heterologous expression of ptn gene cluster and RT-PCR results for each operon in native and wild-type hosts. (d) The proposed platencin biosynthetic pathway, along with several shunt metabolites isolated from a heterologous expression strain. Values show in red are titers in heterologous host, while those shown in green are titers in the native producer. (e) Illustration of behavior-matching via part replacement during host transfer. Graphs represent empirical characterization of genetic parts in native host (green), and new host (red). Landscape graphs show effect on gene clusters performance, as measured by titer of final metabolite, in a multivariate system.

Figure 5

Advanced regulation relevant to NP biosynthesis. Examples include (a) inducible promoters for NP producing organisms^,, (b) a mammalian genetic circuit responsive to a bacterial metabolite, (c) dynamic modeling results for a synthetic pathway for para-aminostyrene production, (d) a dynamic feedback/feedforward circuit for monitoring fatty acid ethyl ester production in E. coli, (e) a resource allocation system for controlling transcription of a heterologous neurosporene operon in different hosts, (f) a genetic reset timer for controlled sedimentation in yeast, and (g) multiplexed transcriptional control of the violacein biosynthetic pathway using CRISPRi/CRISPRa. For dynamic modeling example (c), graphs show frequencies of expected yields for designs with static regulation (top), dynamic regulation (middle), or for the particular pattern of dynamic regulation pictured at left (bottom).

Figure 6

Multiplexed genome editing with CRISPR/Cas9. (a) Minimal genome editing construct design, including (i) sgRNA, (ii) S. pyogenes Cas9, and (iii) optional ‘repair fragment’. Three routes to DNA repair are shown, including homologous recombination (HR, left), alternative end-joining (AEJ, center), and non-homologous end-joining (NHEJ, center). (b) Applications of CRISPR-mediated genome editing in Streptomyces. Graph at bottom shows reported efficiencies for experiments grouped by application with background color matching illustrations above. Protocol differences are labeled below graph, and data points are colored according to published study (blue, red, green). (c) Example of multiplexed CRISPR editing for engineering mevalonate levels. Bar graph at bottom shows editing efficiency (grey) and mevalonate levels (green), averaged across multiple different combinations of gene deletions (number of combinations indicated in parentheses).

Figure 7

Diverse applications of engineering NP biosynthesis. Structures of NPs are shown alongside a representation of their BGCs with native producing organisms noted.