Metabolite damage and repair in metabolic engineering design

The necessarily sharp focus of metabolic engineering and metabolic synthetic biology on pathways and their fluxes has tended to divert attention from the damaging enzymatic and chemical side-reactions that pathway metabolites can undergo. Although historically overlooked and underappreciated, such metabolite damage reactions are now known to occur throughout metabolism and to generate (formerly enigmatic) peaks detected in metabolomics datasets. It is also now known that metabolite damage is often countered by dedicated repair enzymes that undo or prevent it. Metabolite damage and repair are highly relevant to engineered pathway design: metabolite damage reactions can reduce flux rates and product yields, and repair enzymes can provide robust, host-independent solutions. Herein, after introducing the core principles of metabolite damage and repair, we use case histories to document how damage and repair processes affect efficient operation of engineered pathways – particularly those that are heterologous, non-natural, or cell-free. We then review how metabolite damage reactions can be predicted, how repair reactions can be prospected, and how metabolite damage and repair can be built into genome-scale metabolic models. Lastly, we propose a versatile ‘plug and play’ set of well-characterized metabolite repair enzymes to solve metabolite damage problems known or likely to occur in metabolic engineering and synthetic biology projects.     

Dynamic metabolic engineering: New strategies for developing responsive cell factories

Metabolic engineering strategies have enabled improvements in yield and titer for a variety of valuable small molecules produced naturally in microorganisms, as well as those produced via heterologous pathways. Typically, the approaches have been focused on up‐ and downregulation of genes to redistribute steady‐state pathway fluxes, but more recently a number of groups have developed strategies for dynamic regulation, which allows rebalancing of fluxes according to changing conditions in the cell or the fermentation medium. This review highlights some of the recently published work related to dynamic metabolic engineering strategies and explores how advances in high‐throughput screening and synthetic biology can support development of new dynamic systems. Dynamic gene expression profiles allow trade‐offs between growth and production to be better managed and can help avoid build‐up of undesired intermediates. The implementation is more complex relative to static control, but advances in screening techniques and DNA synthesis will continue to drive innovation in this field.     

Synthetic Escherichia coli consortia engineered for syntrophy demonstrate enhanced biomass productivity

Synthetic Escherichia coli consortia engineered for syntrophy demonstrated enhanced biomass productivity relative to monocultures. Binary consortia were designed to mimic a ubiquitous, naturally occurring ecological template of primary productivity supported by secondary consumption. The synthetic consortia replicated this evolution-proven strategy by combining a glucose positive E. coli strain, which served as the system's primary producer, with a glucose negative E. coli strain which consumed metabolic byproducts from the primary producer. The engineered consortia utilized strategic division of labor to simultaneously optimize multiple tasks enhancing overall culture performance. Consortial interactions resulted in the emergent property of enhanced system biomass productivity which was demonstrated with three distinct culturing systems: batch, chemostat and biofilm growth. Glucose-based biomass productivity increased by ∼15, 20 and 50% compared to appropriate monoculture controls for these three culturing systems, respectively. Interestingly, the consortial interactions also produced biofilms with predictable, self-assembling, laminated microstructures. This study establishes a metabolic engineering paradigm which can be easily adapted to existing E. coli based bioprocesses to improve productivity based on a robust ecological theme.     

Optimizing metabolic pathways by screening for feasible synthetic reactions

Background

Reconstruction of genome-scale metabolic networks has resulted in models capable of reproducing experimentally observed biomass yield/growth rates and predicting the effect of alterations in metabolism for biotechnological applications. The existing studies rely on modifying the metabolic network of an investigated organism by removing or inserting reactions taken either from evolutionary similar organisms or from databases of biochemical reactions (e.g., KEGG). A potential disadvantage of these knowledge-driven approaches is that the result is biased towards known reactions, as such approaches do not account for the possibility of including novel enzymes, together with the reactions they catalyze.

Results

Here, we explore the alternative of increasing biomass yield in three model organisms, namely Bacillus subtilis, Escherichia coli, and Hordeum vulgare, by applying small, chemically feasible network modifications. We use the predicted and experimentally confirmed growth rates of the wild-type networks as reference values and determine the effect of inserting mass-balanced, thermodynamically feasible reactions on predictions of growth rate by using flux balance analysis.

Conclusions

While many replacements of existing reactions naturally lead to a decrease or complete loss of biomass production ability, in all three investigated organisms we find feasible modifications which facilitate a significant increase in this biological function. We focus on modifications with feasible chemical properties and a significant increase in biomass yield. The results demonstrate that small modifications are sufficient to substantially alter biomass yield in the three organisms. The method can be used to predict the effect of targeted modifications on the yield of any set of metabolites (e.g., ethanol), thus providing a computational framework for synthetic metabolic engineering.     

Expanding the metabolic engineering toolbox with directed evolution

Cellular systems can be engineered into factories that produce high-value chemicals from renewable feedstock. Such an approach requires an expanded toolbox for metabolic engineering. Recently, protein engineering and directed evolution strategies have started to play a growing and critical role within metabolic engineering. This review focuses on the various ways in which directed evolution can be applied in conjunction with metabolic engineering to improve product yields. Specifically, we discuss the application of directed evolution on both catalytic and non-catalytic traits of enzymes, on regulatory elements, and on whole genomes in a metabolic engineering context. We demonstrate how the goals of metabolic pathway engineering can be achieved in part through evolving cellular parts as opposed to traditional approaches that rely on gene overexpression and deletion. Finally, we discuss the current limitations in screening technology that hinder the full implementation of a metabolic pathway-directed evolution approach.     

Ensemble Modeling for Robustness Analysis in engineering non-native metabolic pathways

Metabolic pathways in cells must be sufficiently robust to tolerate fluctuations in expression levels and changes in environmental conditions. Perturbations in expression levels may lead to system failure due to the disappearance of a stable steady state. Increasing evidence has suggested that biological networks have evolved such that they are intrinsically robust in their network structure. In this article, we presented Ensemble Modeling for Robustness Analysis (EMRA), which combines a continuation method with the Ensemble Modeling approach, for investigating the robustness issue of non-native pathways. EMRA investigates a large ensemble of reference models with different parameters, and determines the effects of parameter drifting until a bifurcation point, beyond which a stable steady state disappears and system failure occurs. A pathway is considered to have high bifurcational robustness if the probability of system failure is low in the ensemble. To demonstrate the utility of EMRA, we investigate the bifurcational robustness of two synthetic central metabolic pathways that achieve carbon conservation: non-oxidative glycolysis and reverse glyoxylate cycle. With EMRA, we determined the probability of system failure of each design and demonstrated that alternative designs of these pathways indeed display varying degrees of bifurcational robustness. Furthermore, we demonstrated that target selection for flux improvement should consider the trade-offs between robustness and performance.     

Opportunities for yeast metabolic engineering: Lessons from synthetic biology

Constant progress in genetic engineering has given rise to a number of promising areas of research that facilitated the expansion of industrial biotechnology. The field of metabolic engineering, which utilizes genetic tools to manipulate microbial metabolism to enhance the production of compounds of interest, has had a particularly strong impact by providing new platforms for chemical production. Recent developments in synthetic biology promise to expand the metabolic engineering toolbox further by creating novel biological components for pathway design. The present review addresses some of the recent advances in synthetic biology and how these have the potential to affect metabolic engineering in the yeast Saccharomyces cerevisiae. While S. cerevisiae for years has been a robust industrial organism and the target of multiple metabolic engineering trials, its potential for synthetic biology has remained relatively unexplored and further research in this field could strongly contribute to industrial biotechnology. This review also addresses are general considerations for pathway design, ranging from individual components to regulatory systems, overall pathway considerations and whole-organism engineering, with an emphasis on potential contributions of synthetic biology to these areas. Some examples of applications for yeast synthetic biology and metabolic engineering are also discussed.     

Fifteen years of large scale metabolic modeling of yeast: developments and impacts

Since the first large-scale reconstruction of the Saccharomyces cerevisiae metabolic network 15 years ago the development of yeast metabolic models has progressed rapidly, resulting in no less than nine different yeast genome-scale metabolic models. Here we review the historical development of large-scale mathematical modeling of yeast metabolism and the growing scope and impact of applications of these models in four different areas: as guide for metabolic engineering and strain improvement, as a tool for biological interpretation and discovery, applications of novel computational framework and for evolutionary studies.     

Evaluating enzymatic synthesis of small molecule drugs

There have been many achievements in applying biochemical synthetic routes to the synthesis of commodity chemicals. However, most of these endeavors have focused on optimizing and increasing the yields of naturally existing pathways. We sought to evaluate the potential for biosynthesis beyond the limits of known biochemistry towards the production of small molecule drugs that do not exist in nature. Because of the potential for improved yields compared to total synthesis, and therefore lower manufacturing costs, we focused on drugs for diseases endemic to many resource poor regions, like tuberculosis and HIV. Using generalized biochemical reaction rules, we were able to design biochemical pathways for the production of eight small molecule drugs or drug precursors and identify potential enzyme-substrate pairs for nearly every predicted reaction. All pathways begin from native metabolites, abrogating the need for specialized precursors. The simulated pathways showed several trends with the sequential ordering of reactions as well as the types of chemistries used. For some compounds, the main obstacles to finding feasible biochemical pathways were the lack of appropriate, natural starting compounds and a low diversity of biochemical coupling reactions necessary to synthesize molecules with larger molecular size.     

Automated engineering of synthetic metabolic pathways for efficient biomanufacturing

Metabolic engineering involves the engineering and optimization of processes from single-cell to fermentation in order to increase production of valuable chemicals for health, food, energy, materials and others. A systems approach to metabolic engineering has gained traction in recent years thanks to advances in strain engineering, leading to an accelerated scaling from rapid prototyping to industrial production. Metabolic engineering is nowadays on track towards a truly manufacturing technology, with reduced times from conception to production enabled by automated protocols for DNA assembly of metabolic pathways in engineered producer strains. In this review, we discuss how the success of the metabolic engineering pipeline often relies on retrobiosynthetic protocols able to identify promising production routes and dynamic regulation strategies through automated biodesign algorithms, which are subsequently assembled as embedded integrated genetic circuits in the host strain. Those approaches are orchestrated by an experimental design strategy that provides optimal scheduling planning of the DNA assembly, rapid prototyping and, ultimately, brings forward an accelerated Design-Build-Test-Learn cycle and the overall optimization of the biomanufacturing process. Achieving such a vision will address the increasingly compelling demand in our society for delivering valuable biomolecules in an affordable, inclusive and sustainable bioeconomy.     

Improving metabolic efficiency of the reverse beta-oxidation cycle by balancing redox cofactor requirement

Previous studies have made many exciting achievements on pushing the functional reversal of beta-oxidation cycle (r-BOX) to more widespread adoption for synthesis of a wide variety of fuels and chemicals. However, the redox cofactor requirement for the efficient operation of r-BOX remains unclear. In this work, the metabolic efficiency of r-BOX for medium-chain fatty acid (C₆-C₁₀, MCFA) production was optimized by redox cofactor engineering. Stoichiometric analysis of the r-BOX pathway and further experimental examination identified NADH as a crucial determinant of r-BOX process yield. Furthermore, the introduction of formate dehydrogenase from Candida boidinii using fermentative inhibitor byproduct formate as a redox NADH sink improved MCFA titer from initial 1.2 g/L to 3.1 g/L. Moreover, coupling of increasing the supply of acetyl-CoA with NADH to achieve fermentative redox balance enabled product synthesis at maximum titers. To this end, the acetate re-assimilation pathway was further optimized to increase acetyl-CoA availability associated with the new supply of NADH. It was found that the acetyl-CoA synthetase activity and intracellular ATP levels constrained the activity of acetate re-assimilation pathway, and 4.7 g/L of MCFA titer was finally achieved after alleviating these two limiting factors. To the best of our knowledge, this represented the highest titer reported to date. These results demonstrated that the key constraint of r-BOX was redox imbalance and redox engineering could further unleash the lipogenic potential of this cycle. The redox engineering strategies could be applied to acetyl-CoA-derived products or other bio-products requiring multiple redox cofactors for biosynthesis.     

Systems metabolic engineering: Genome-scale models and beyond

The advent of high throughput genome-scale bioinformatics has led to an exponential increase in available cellular system data. Systems metabolic engineering attempts to use data-driven approaches – based on the data collected with high throughput technologies – to identify gene targets and optimize phenotypical properties on a systems level. Current systems metabolic engineering tools are limited for predicting and defining complex phenotypes such as chemical tolerances and other global, multigenic traits. The most pragmatic systems-based tool for metabolic engineering to arise is the in silico genome-scale metabolic reconstruction. This tool has seen wide adoption for modeling cell growth and predicting beneficial gene knockouts, and we examine here how this approach can be expanded for novel organisms. This review will highlight advances of the systems metabolic engineering approach with a focus on de novo development and use of genome-scale metabolic reconstructions for metabolic engineering applications. We will then discuss the challenges and prospects for this emerging field to enable model-based metabolic engineering. Specifically, we argue that current state-of-the-art systems metabolic engineering techniques represent a viable first step for improving product yield that still must be followed by combinatorial techniques or random strain mutagenesis to achieve optimal cellular systems.     

Promiscuous phosphoketolase and metabolic rewiring enables novel non-oxidative glycolysis in yeast for high-yield production of acetyl-CoA derived products

Carbon-conserving pathways have the potential of increasing product yields in biotechnological processes. The aim of this project was to investigate the functionality of a novel carbon-conserving pathway that produces 3 mol of acetyl-CoA from fructose-6-phosphate without carbon loss in the yeast Saccharomyces cerevisiae. This cyclic pathway relies on a generalist phosphoketolase (Xfspk), which can convert xylulose-5-phosphate, fructose-6-phosphate and sedoheptulose-7-phosphate (S7P) to acetyl phosphate. This cycle is proposed to overcome bottlenecks from the previously reported non-oxidative glycolysis (NOG) cycle. Here, in silico simulations showed accumulation of S7P in the NOG cycle, which was resolved by blocking the non-oxidative pentose phosphate pathway and introducing Xfspk and part of the riboneogenesis pathway. To implement this, a transketolase and transaldolase deficient S. cerevisiae was generated and a cyclic pathway, the Glycolysis AlTernative High Carbon Yield Cycle (GATHCYC), was enabled through xfspk expression and sedoheptulose bisphosphatase (SHB17) overexpression. Flux through the GATHCYC was demonstrated in vitro with a phosphoketolase assay on crude cell free extracts, and in vivo by constructing a strain that was dependent on a functional pathway to survive. Finally, we showed that introducing the GATHCYC as a carbon-conserving route for 3-hydroxypropionic acid (3-HP) production resulted in a 109% increase in 3-HP titers when the glucose was exhausted compared to the phosphoketolase route only.     

The imminent role of protein engineering in synthetic biology

Protein engineering has for decades been a powerful tool in biotechnology for generating vast numbers of useful enzymes for industrial applications. Today, protein engineering has a crucial role in advancing the emerging field of synthetic biology, where metabolic engineering efforts alone are insufficient to maximize the full potential of synthetic biology. This article reviews the advancements in protein engineering techniques for improving biocatalytic properties to optimize engineered pathways in host systems, which are instrumental to achieve high titer production of target molecules. We also discuss the specific means by which protein engineering has improved metabolic engineering efforts and provide our assessment on its potential to continue to advance biology engineering as a whole.     

A synthetic C2 auxotroph of Pseudomonas putida for evolutionary engineering of alternative sugar catabolic routes

Acetyl-coenzyme A (AcCoA) is a metabolic hub in virtually all living cells, serving as both a key precursor of essential biomass components and a metabolic sink for catabolic pathways for a large variety of substrates. Owing to this dual role, tight growth-production coupling schemes can be implemented around the AcCoA node. Building on this concept, a synthetic C2 auxotrophy was implemented in the platform bacterium Pseudomonas putida through an in silico-informed engineering approach. A growth-coupling strategy, driven by AcCoA demand, allowed for direct selection of an alternative sugar assimilation route—the phosphoketolase (PKT) shunt from bifidobacteria. Adaptive laboratory evolution forced the synthetic P. putida auxotroph to rewire its metabolic network to restore C2 prototrophy via the PKT shunt. Large-scale structural chromosome rearrangements were identified as possible mechanisms for adjusting the network-wide proteome profile, resulting in improved PKT-dependent growth phenotypes. ¹³C-based metabolic flux analysis revealed an even split between the native Entner-Doudoroff pathway and the synthetic PKT bypass for glucose processing, leading to enhanced carbon conservation. These results demonstrate that the P. putida metabolism can be radically rewired to incorporate a synthetic C2 metabolism, creating novel network connectivities and highlighting the importance of unconventional engineering strategies to support efficient microbial production.     

Combinatorial pathway engineering of Bacillus subtilis for production of structurally defined and homogeneous chitooligosaccharides

Chitooligosaccharides (COSs) have a widespread range of biological functions and an incredible potential for various pharmaceutical and agricultural applications. Although several physical, chemical, and biological techniques have been reported for COSs production, it is still a challenge to obtain structurally defined COSs with defined polymerization (DP) and acetylation patterns, which hampers the specific characterization and application of COSs. Herein, we achieved the de novo production of structurally defined COSs using combinatorial pathway engineering in Bacillus subtilis. Specifically, the COSs synthase NodC from Azorhizobium caulinodans was overexpressed in B. subtilis, leading to 30 ± 0.86 mg/L of chitin oligosaccharides (CTOSs), the homo-oligomers of N-acetylglucosamine (GlcNAc) with a well-defined DP lower than 6. Then introduction of a GlcNAc synthesis module to promote the supply of the sugar acceptor GlcNAc, reduced CTOSs production, which suggested that the activity of COSs synthase NodC and the supply of sugar donor UDP-GlcNAc may be the limiting steps for CTOSs synthesis. Therefore, 6 exogenous COSs synthase candidates were examined, and the nodCM from Mesorhizobium loti yielded the highest CTOSs titer of 560 ± 16 mg/L. Finally, both the de novo pathway and the salvage pathway of UDP-GlcNAc were engineered to further promote the biosynthesis of CTOSs. The titer of CTOSs in 3-L fed-batch bioreactor reached 4.82 ± 0.11 g/L (85.6% CTOS5, 7.5% CTOS4, 5.3% CTOS3 and 1.6% CTOS2), which was the highest ever reported. This is the first report proving the feasibility of the de novo production of structurally defined CTOSs by synthetic biology, and provides a good starting point for further engineering to achieve the commercial production.     

A systems level engineered E. coli capable of efficiently producing L-phenylalanine

The biosynthesis of L-phenylalanine (Phe) is one of the most complicated amino acid synthesis pathways. In this study, the engineering of Phe producer was carried out to illustrate the effectiveness of systems level engineering: (1) inactivated glucose specific phosphoenolpyruvate-carbohydrate phosphotransferase (PTS) system by inactivation of crr to moderate the glucose uptake rate to reduce overflow metabolism; (2) genetic switch on or off the expression of phe^fbr, aroG15, ydiB, aroK, and tyrB to increase the supply of precursors; (3) employed a tyrA mutant strain to reduce carbon diversion and to result in non-growing cells; (4) enhanced the efflux of Phe by overexpressing yddG to shift equilibrium towards Phe synthesis and to release the feedback regulation in Phe synthesis. The mutants in PTS were firstly compared and a crr⁻ mutant was firstly screened. The mutant AroG15 was demonstrated to a thermostable mutant. The strains expressing yddG excreted Phe into the medium at higher rate and less intracellular Phe accumulated. By systems level engineering, an engineered Phe producer achieved 47.0 g/L Phe with a yield of 0.252 g/g which was the highest under the non-optimized fermentation condition.     

Model reduction of genome-scale metabolic models as a basis for targeted kinetic models

Constraint-based, genome-scale metabolic models are an essential tool to guide metabolic engineering. However, they lack the detail and time dimension that kinetic models with enzyme dynamics offer. Model reduction can be used to bridge the gap between the two methods and allow for the integration of kinetic models into the Design-Built-Test-Learn cycle. Here we show that these reduced size models can be representative of the dynamics of the original model and demonstrate the automated generation and parameterisation of such models. Using these minimal models of metabolism could allow for further exploration of dynamic responses in metabolic networks.     

Modular and selective biosynthesis of gasoline-range alkanes

Typical renewable liquid fuel alternatives to gasoline are not entirely compatible with current infrastructure. We have engineered Escherichia coli to selectively produce alkanes found in gasoline (propane, butane, pentane, heptane, and nonane) from renewable substrates such as glucose or glycerol. Our modular pathway framework achieves carbon-chain extension by two different mechanisms. A fatty acid synthesis route is used to generate longer chains heptane and nonane, while a more energy efficient alternative, reverse-β-oxidation, is used for synthesis of propane, butane, and pentane. We demonstrate that both upstream (thiolase) and intermediate (thioesterase) reactions can act as control points for chain-length specificity. Specific free fatty acids are subsequently converted to alkanes using a broad-specificity carboxylic acid reductase and a cyanobacterial aldehyde decarbonylase (AD). The selectivity obtained by different module pairings provides a foundation for tuning alkane product distribution for desired fuel properties. Alternate ADs that have greater activity on shorter substrates improve observed alkane titer. However, even in an engineered host strain that significantly reduces endogenous conversion of aldehyde intermediates to alcohol byproducts, AD activity is observed to be limiting for all chain lengths. Given these insights, we discuss guiding principles for pathway selection and potential opportunities for pathway improvement.