Original Research ArticleA cell-free framework for rapid biosynthetic pathway prototyping and enzyme discovery
Introduction
For decades scientists and engineers have turned to engineering biological systems to help meet societal needs in energy, medicine, materials, and more (Bornscheuer et al., 2012, Fritz et al., 2010, Curran and Alper, 2012, Rollié et al., 2012). This has been an attractive, sustainable way to produce small molecules, especially when chemical synthesis is untenable (Erickson et al., 2012, Nielsen et al., 2014). The ability to harness organisms that naturally produce molecules of interest has expanded the available chemical palate (Demain, 2014, Harvey et al., 2015). Often when natural producers are insufficient for production at the optimal titer (g l−1), yield, or volumetric productivity (g l−1 h−1), engineers seek to design biosynthetic pathways and regulatory processes in cells to meet certain manufacturing criteria (Kern et al., 2007, Nielsen, 2001). For example, introducing heterologous pathways into model microorganisms and engineering them to maximize a particular biosynthesis has led to large scale production of 1,3-propanediol, farnesene, and artemisinin with many more on their way to market (Nielsen et al., 2014, Hodgman and Jewett, 2012). Efforts to make these molecules have resulted in success, but not without a great deal of challenges.
Bringing a biosynthetic molecule to market usually involves countless hours of design-build-test (DBT) cycles (Kwok, 2010). The production of n-butanol is a prime example of these challenges. A series of Clostridia species are natural producers of n-butanol during acetone–butanol–ethanol fermentation, and Clostridia acetobutylicum and Clostridia beijerinckii are two of which are commonly used in commercial n-butanol plants (Green, 2011). However, these species are difficult to engineer because of a biphasic metabolism, unknown regulation patterns, and a limited number of species-specific engineering tools (Lutke-Eversloh and Bahl, 2011). Heterologous expression of Clostridia metabolism in model microorganisms like Escherichia coli and Saccharomyces cerevisiae allows n-butanol production to be more easily engineered but can be accompanied by lower titers (Atsumi et al., 2008, Steen et al., 2008). Starting with heterologous expression of the n-butanol pathway as a baseline, scientists have been able to increase titers dramatically by knocking out genes from genomes (Atsumi et al., 2008), increasing redox driving forces by introducing pathway-independent enzymes (Shen et al., 2011), and identifying homologous enzymes with better activities (Bond-Watts et al., 2011). Years of iterative metabolic engineering led to these advances, but titers are still not high enough and scale-up is often too unpredictable to outcompete natural producers for commercial production (Dong et al., 2015). As is the same for many biosynthetic pathways, we cannot quickly enough identify optimal biosynthetic systems and discover the best sets of enzymes that work together as a group. Therefore, metabolic engineering remains costly and time-consuming (Keasling, 2010, Keasling, 2012).
A key challenge in metabolic engineering is balancing the tug-of-war that exists between the cell׳s physiological and evolutionary objectives on one side and the engineer׳s process objectives on the other. Put another way, it is very difficult to balance intracellular fluxes to optimally satisfy a very active synthetic pathway while the machinery of the cell is functioning to maintain reproductive viability. Other challenges include: (i) the need for reliable computational selection and design of enzyme homologs for pathway design, (ii) the limited number of feasible homologs and genetic constructs that can be searched in any one project, and (iii) the unknown effects of optimal pathway enzyme expression on the entire metabolic system (Jensen and Keasling, 2014, Dai and Nielsen, 2015, Lee and Kim, 2015).
Many established and emerging technologies seek to address these challenges. For example, metabolic flux analysis and genome engineering offer generalized capabilities to modify living organisms for improving product titers (Lee et al., 2012, Yadav et al., 2012). In addition, coupling machine-learning algorithms to multiplexed designs can accelerate efforts to rationally engineer cells (Smanski et al., 2014). However, DBT cycle time remains a limitation (Boyle and Silver, 2012). In vitro systems offer a complementary, yet underutilized approach to speed up DBT cycles with some potential advantages (Hodgman and Jewett, 2012, Carlson et al., 2012, Sun et al., 2014, Siegal-Gaskins et al., 2014). For example, the open reaction environment allows for the addition of components such as cofactors and intermediates at any time during a cell-free reaction, which can be maintained at precise concentrations. In addition, cell-free systems have no cell viability constraints. Furthermore, the cell-free format permits DBT iterations without the need to reengineer organisms (Sun et al., 2014), with the potential to reduce DBT cycle time (Siegal-Gaskins et al., 2014). Cell-free metabolic engineering (CFME), or using cell-free techniques to aid metabolic engineering efforts, is emerging as a complementary approach to existing strategies for carrying out biomolecular transformations of interest with in vitro ensembles of catalytic proteins, prepared from purified enzymes or crude lysates of cells (Dudley et al., 2015, Zhang, 2015, You and Zhang, 2013, Guterl et al., 2012, Kay and Jewett, 2015, Krutsakorn et al., 2013, Ninh et al., 2015, Welch and Scopes, 1985).
In this work, we develop a cell-free protein synthesis driven metabolic engineering (CFPS-ME) framework to accelerate DBT cycles for optimizing and debugging biosynthetic pathways (Fig. 1A). The foundational principle is that we can construct discrete metabolic pathways through combinatorial and modular assembly of lysates containing enzyme components produced by overexpression in the lysate chassis strain or by cell-free protein synthesis (CFPS). We focus on using CFPS because these systems can help address the growing demand for simple, inexpensive, and efficient protein production technologies for a wide array of applications (Hodgman and Jewett, 2012, Carlson et al., 2012, Swartz, 2012, Dodevski et al., 2015, Henrich et al., 2015, Zemella et al., 2015, Noireaux et al., 2003). In addition, processes that take days or weeks to design, prepare, and execute in cells can be done more rapidly in a cell-free system, because no time-consuming cloning steps are needed (Goshima et al., 2008). Three recent advances enable the use of CFPS for CFME. First, Jewett et al. (2008) demonstrated the ability to stimulate highly active energy and cofactor regeneration pathways in crude cell lysates. Second, Kay and Jewett (2015) showed that crude cell lysate based cell-free systems from E. coli could fuel highly active heterologous metabolic transformations. Third, Dudley and Jewett established the ability to build a heterologous biosynthetic pathway by mixing lysates each containing individually overexpressed heterologous enzymes (in preparation). The mix-and-match approach has many advantages including only needing to express one enzyme in each strain, not needing to fine-tune expression, and being able to directly monitor and sample the reaction environment. Here, we extend this approach by demonstrating modular assembly of pathways through the ability to enrich lysates with biosynthetic enzymes using well-defined experimental conditions and CFPS. It is important to note that our goal in this work was not to develop cell-free systems for the highest product titer, an engineered strain for best in vivo synthesis of n-butanol, or industrial applicability. However, we do show that CFPS-ME offers an even faster approach (hours rather than days) for building pathways directly in lysates for the purpose of enzyme selection and pathway design.
To demonstrate CFPS-ME, we selected the model n-butanol biosynthetic pathway derived from Clostridia metabolism involving CoA intermediates (Fig. 1B). Endogenous glycolytic enzymes convert glucose to acetyl-CoA, the starting intermediate for n-butanol synthesis, another E. coli enzyme takes acetyl-CoA to acetoacetyl-CoA, and heterologous enzymes convert acetoacetyl-CoA to n-butanol. We first show the ability to mix five crude lysates each with selectively overexpressed enzymes to activate the entire 17-step n-butanol production pathway in vitro with high yield and productivities. We then establish the CFPS-ME concept by modularly building the n-butanol pathway with lysates harboring heterologous pathway enzymes expressed by CFPS or having been overexpressed in the chassis source strain. We apply this framework to rapidly screen enzymes for optimal pathway operation and enzyme discovery. We expect that the CFPS-ME framework will increase the resolution at which we can manipulate biosynthetic pathways by examining enzyme kinetics, measuring metabolic flux, determining catalyst stability, studying redox effects, and prototyping metabolism.
Section snippets
Bacterial strains and plasmids
E. coli NEB Turbo™ (NEB) was used in plasmid cloning transformations and for plasmid preparation. E. coli BL21(DE3) (NEB) was used for protein overexpression and for preparation of all extracts (see Supplementary Table 1 for strain details). A modified version of pET-22b (Novagen/EMD Millipore), used in previous studies (Kay and Jewett, 2015), was used for all constructs for in vivo over-expression of proteins. For in vitro expression of proteins, the pJL1 vector was used. Carbenicillin (100 μg ml
Results
In developing a framework for biosynthetic pathway prototyping, we constructed a 17-step pathway for the production of n-butanol. n-butanol synthesis was selected as a model because of its importance as a potential biofuel, it is easily quantified by HPLC, and it has multiple heterologous steps. We sought to combine E. coli׳s endogenous 11-step glycolytic pathway from glucose to acetyl-CoA (AcCoA) with the Clostridia-derived six-step n-butanol pathway from AcCoA (Fig. 1B). The idea that natural
Discussion
In this study, we developed a new cell-free framework for prototyping biosynthetic pathways and screening enzymes. In one scenario, we overexpress individual pathway components in cells, lyse these cells, and mix and match lysates in cell-free cocktails to study biochemical pathway performance. In a distinct thrust from typical in vitro systems, our approach allows us to study heterologous pathways in the context of native metabolism. In another scenario, we bypass in vivo expression altogether
Author contributions
A.S.K. and M.C.J. conceived and designed the experiments. A.S.K. performed all of the experimental work. A.S.K. and M.C.J. wrote the manuscript.
Competing financial Interests
The authors declare no competing financial interests.
Acknowledgments
This work is funded by the DARPA Program (D14PC00005/0001). Additional support was from the David and Lucile Packard Foundation and the Camille Dreyfus Teacher Scholar Award (to M.C.J.). A.S.K. is an NSF Graduate Fellow.
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