Controlling metabolic flux by toehold-mediated strand displacement

To maximize desired products in engineered cellular factories it is often necessary to optimize metabolic flux. While a number of works have focused on metabolic pathway enhancement through genetic regulators and synthetic scaffolds, these approaches require time-intensive design and optimization with limited versatility and capacity for scale-up. Recently, nucleic-acid nanotechnology has emerged as an encouraging approach to overcome these limitations and create systems for modular programmable control of metabolic flux. Using toehold-mediated strand displacement (TMSD), nucleic acid constructs can be made into dynamic devices that recognize specific biomolecular triggers for conditional control of gene regulation as well as design of dynamic synthetic scaffolds. This review will consider the various approaches that have been used thus far to control metabolic flux using toehold-gated devices.

Computer-Aided Whole-Cell Design: Taking a Holistic Approach by Integrating Synthetic With Systems Biology

Computer-aided design (CAD) for synthetic biology promises to accelerate the rational and robust engineering of biological systems. It requires both detailed and quantitative mathematical and experimental models of the processes to (re)design biology, and software and tools for genetic engineering and DNA assembly. Ultimately, the increased precision in the design phase will have a dramatic impact on the production of designer cells and organisms with bespoke functions and increased modularity. CAD strategies require quantitative models of cells that can capture multiscale processes and link genotypes to phenotypes. Here, we present a perspective on how whole-cell, multiscale models could transform design-build-test-learn cycles in synthetic biology. We show how these models could significantly aid in the design and learn phases while reducing experimental testing by presenting case studies spanning from genome minimization to cell-free systems. We also discuss several challenges for the realization of our vision. The possibility to describe and build whole-cells in silico offers an opportunity to develop increasingly automatized, precise and accessible CAD tools and strategies.

Metabolic pathway engineering: Perspectives and applications

BACKGROUND

Metabolic engineering aims at contriving microbes as biocatalysts for enhanced and cost-effective production of countless secondary metabolites. These secondary metabolites can be treated as the resources of industrial chemicals, pharmaceuticals and fuels. Plants are also crucial targets for metabolic engineers to produce necessary secondary metabolites. Metabolic engineering of both microorganism and plants also contributes towards drug discovery. In order to implement advanced metabolic engineering techniques efficiently, metabolic engineers should have detailed knowledge about cell physiology and metabolism. Principle behind methodologies: Genome-scale mathematical models of integrated metabolic, signal transduction, gene regulatory and protein-protein interaction networks along with experimental validation can provide such knowledge in this context. Incorporation of omics data into these models is crucial in the case of drug discovery. Inverse metabolic engineering and metabolic control analysis (MCA) can help in developing such models. Artificial intelligence methodology can also be applied for efficient and accurate metabolic engineering.

CONCLUSION

In this review, we discuss, at the beginning, the perspectives of metabolic engineering and its application on microorganism and plant leading to drug discovery. At the end, we elaborate why inverse metabolic engineering and MCA are closely related to modern metabolic engineering. In addition, some crucial steps ensuring efficient and optimal metabolic engineering strategies have been discussed. Moreover, we explore the use of genomics data for the activation of silent metabolic clusters and how it can be integrated with metabolic engineering. Finally, we exhibit a few applications of artificial intelligence to metabolic engineering.

Biosensors design in yeast and applications in metabolic engineering

Engineering microbial cell factories is a potential approach of sustainable production of chemicals, fuels and pharmaceuticals. However, testing the production of molecules in high throughput is still a time-consuming and laborious process since product synthesis usually does not confer a clear phenotype. Therefore, it is necessary to develop new techniques for fast high-producer screening. Genetically encoded biosensors are considered to be promising devices for high-throughput analysis owing to their ability to sense metabolites and couple detection to an actuator, thereby facilitating the rapid detection of small molecules at single-cell level. Here, we review recent advances in the design and engineering of biosensors in Saccharomyces cerevisiae, and their applications in metabolic engineering. Three types of biosensor are introduced in this review: transcription factor based, RNA-based and enzyme-coupled biosensors. The studies to improve the features of biosensors are also described. Moreover, we summarized their metabolic engineering applications in dynamic regulation and high producer selection. Current challenges in biosensor design and future perspectives on sensor applications are also discussed.

Optimization of hydrogenobyrinic acid biosynthesis in Escherichia coli using multi-level metabolic engineering strategies

BACKGROUND

Hydrogenobyrinic acid is a key intermediate of the de-novo aerobic biosynthesis pathway of vitamin B₁₂. The introduction of a heterologous de novo vitamin B₁₂ biosynthesis pathway in Escherichia coli offers an alternative approach for its production. Although E. coli avoids major limitations that currently faced by industrial producers of vitamin B₁₂, such as long growth cycles, the insufficient supply of hydrogenobyrinic acid restricts industrial vitamin B₁₂ production.

RESULTS

By designing combinatorial ribosomal binding site libraries of the hemABCD genes in vivo, we found that their optimal relative translational initiation rates are 10:1:1:5. The transcriptional coordination of the uroporphyrinogen III biosynthetic module was realized by promoter engineering of the hemABCD operon. Knockdown of competitive heme and siroheme biosynthesis pathways by RBS engineering enhanced the hydrogenobyrinic acid titer to 20.54 and 15.85 mg L^-1, respectively. Combined fine-tuning of the heme and siroheme biosynthetic pathways enhanced the hydrogenobyrinic acid titer to 22.57 mg L^-1, representing a remarkable increase of 1356.13% compared with the original strain FH215-HBA.

CONCLUSIONS

Through multi-level metabolic engineering strategies, we achieved the metabolic balance of the uroporphyrinogen III biosynthesis pathway, eliminated toxicity due to by-product accumulation, and finally achieved a high HBA titer of 22.57 mg L^-1 in E. coli. This lays the foundation for high-yield production of vitamin B₁₂ in E. coli and will hopefully accelerate its industrial production.

Quorum sensing for population-level control of bacteria and potential therapeutic applications

Quorum sensing (QS), a microbial cell-to-cell communication process, dynamically regulates a variety of metabolism and physiological activities. In this review, we provide an update on QS applications based on autoinducer molecules including acyl-homoserine lactones (AHLs), auto-inducing peptides (AIPs), autoinducer 2 (AI-2) and indole in population-level control of bacteria, and highlight the potential in developing novel clinical therapies. We summarize the development in the combination of various genetic circuits such as genetic oscillators, toggle switches and logic gates with AHL-based QS devices in Gram-negative bacteria. An overview is then offered to the state-of-the-art of much less researched applications of AIP-based QS devices with Gram-positive bacteria, followed by a review of the applications of AI-2 and indole based QS for interspecies communication among microbial communities. Building on these general-purpose QS applications, we highlight the disruptions and manipulations of QS devices as potential clinical therapies for diseases caused by biofilm formation, antibiotic resistance and the phage invasion. The last part of reviewed literature is dedicated to mathematical modelling for QS applications. Finally, the key challenges and future perspectives of QS applications in monoclonal synthetic biology and synthetic ecology are discussed.

Organizing genome engineering for the gigabase scale

Genome-scale engineering holds great potential to impact science, industry, medicine, and society, and recent improvements in DNA synthesis have enabled the manipulation of megabase genomes. However, coordinating and integrating the workflows and large teams necessary for gigabase genome engineering remains a considerable challenge. We examine this issue and recommend a path forward by: 1) adopting and extending existing representations for designs, assembly plans, samples, data, and workflows; 2) developing new technologies for data curation and quality control; 3) conducting fundamental research on genome-scale modeling and design; and 4) developing new legal and contractual infrastructure to facilitate collaboration.

Nanomaterials for Healthcare Biosensing Applications

In recent years, an increasing number of nanomaterials have been explored for their applications in biomedical diagnostics, making their applications in healthcare biosensing a rapidly evolving field. Nanomaterials introduce versatility to the sensing platforms and may even allow mobility between different detection mechanisms. The prospect of a combination of different nanomaterials allows an exploitation of their synergistic additive and novel properties for sensor development. This paper covers more than 290 research works since 2015, elaborating the diverse roles played by various nanomaterials in the biosensing field. Hence, we provide a comprehensive review of the healthcare sensing applications of nanomaterials, covering carbon allotrope-based, inorganic, and organic nanomaterials. These sensing systems are able to detect a wide variety of clinically relevant molecules, like nucleic acids, viruses, bacteria, cancer antigens, pharmaceuticals and narcotic drugs, toxins, contaminants, as well as entire cells in various sensing media, ranging from buffers to more complex environments such as urine, blood or sputum. Thus, the latest advancements reviewed in this paper hold tremendous potential for the application of nanomaterials in the early screening of diseases and point-of-care testing.

Synthesis and analysis of separation processes for extracellular chemicals generated from microbial conversions

Recent advances in metabolic engineering have enabled the production of chemicals via bio-conversion using microbes. However, downstream separation accounts for 60–80% of the total production cost in many cases. Previous work on microbial production of extracellular chemicals has been mainly restricted to microbiology, biochemistry, metabolomics, or techno-economic analysis for specific product examples such as succinic acid, xanthan gum, lycopene, etc. In these studies, microbial production and separation technologies were selected apriori without considering any competing alternatives. However, technology selection in downstream separation and purification processes can have a major impact on the overall costs, product recovery, and purity. To this end, we apply a superstructure optimization based framework that enables the identification of critical technologies and their associated parameters in the synthesis and analysis of separation processes for extracellular chemicals generated from microbial conversions. We divide extracellular chemicals into three categories based on their physical properties, such as water solubility, physical state, relative density, volatility, etc. We analyze three major extracellular product categories (insoluble light, insoluble heavy and soluble) in detail and provide suggestions for additional product categories through extension of our analysis framework. The proposed analysis and results provide significant insights for technology selection and enable streamlined decision making when faced with any microbial product that is released extracellularly. The parameter variability analysis for the product as well as the associated technologies and comparison with novel alternatives is a key feature which forms the basis for designing better bioseparation strategies that have potential for commercial scalability and can compete with traditional chemical production methods.

Bacterial co-culture with cell signaling translator and growth controller modules for autonomously regulated culture composition

Synthetic biology and metabolic engineering have expanded the possibilities for engineered cell-based systems. The addition of non-native biosynthetic and regulatory components can, however, overburden the reprogrammed cells. In order to avoid metabolic overload, an emerging area of focus is on engineering consortia, wherein cell subpopulations work together to carry out a desired function. This strategy requires regulation of the cell populations. Here, we design a synthetic co-culture controller consisting of cell-based signal translator and growth-controller modules that, when implemented, provide for autonomous regulation of the consortia composition. The system co-opts the orthogonal autoinducer AI-1 and AI-2 cell-cell signaling mechanisms of bacterial quorum sensing (QS) to enable cross-talk between strains and a QS signal-controlled growth rate controller to modulate relative population densities. We further develop a simple mathematical model that enables cell and system design for autonomous closed-loop control of population trajectories.

To avoid metabolic overload and divide tasks, synthetic biologists are turning to microbial consortia engineering. Here the authors design a co-culture controller that autonomously regulates population composition.

Build Your Bioprocess on a Solid Strain-β-Carotene Production in Recombinant Saccharomyces cerevisiae

Robust fermentation performance of microbial cell factories is critical for successful scaling of a biotechnological process. From shake flask cultivations to industrial-scale bioreactors, consistent strain behavior is fundamental to achieve the production targets. To assert the importance of this feature, we evaluated the impact of the yeast strain design and construction method on process scalability -from shake flasks to bench-scale fed-batch fermentations- using two recombinant Saccharomyces cerevisiae strains capable of producing β-carotene; SM14 and βcar1.2 strains. SM14 strain, obtained previously from adaptive evolution experiments, was capable to accumulate up to 21 mg/g_DCW of β-carotene in 72 h shake flask cultures; while the βcar1.2, constructed by overexpression of carotenogenic genes, only accumulated 5.8 mg/g_DCW of carotene. Surprisingly, fed-batch cultivation of these strains in 1L bioreactors resulted in opposite performances. βcar1.2 strain reached much higher biomass and β-carotene productivities (1.57 g/L/h and 10.9 mg/L/h, respectively) than SM14 strain (0.48 g/L/h and 3.1 mg/L/h, respectively). Final β-carotene titers were 210 and 750 mg/L after 80 h cultivation for SM14 and βcar1.2 strains, respectively. Our results indicate that these substantial differences in fermentation parameters are mainly a consequence of the exacerbated Crabtree effect of the SM14 strain. We also found that the strategy used to integrate the carotenogenic genes into the chromosomes affected the genetic stability of strains, although the impact was significantly minor. Overall, our results indicate that shake flasks fermentation parameters are poor predictors of the fermentation performance under industrial-like conditions, and that appropriate construction designs and performance tests must be conducted to properly assess the scalability of the strain and the bioprocess.

A sigma factor toolbox for orthogonal gene expression in Escherichia coli

Synthetic genetic sensors and circuits enable programmable control over timing and conditions of gene expression and, as a result, are increasingly incorporated into the control of complex and multi-gene pathways. Size and complexity of genetic circuits are growing, but stay limited by a shortage of regulatory parts that can be used without interference. Therefore, orthogonal expression and regulation systems are needed to minimize undesired crosstalk and allow for dynamic control of separate modules. This work presents a set of orthogonal expression systems for use in Escherichia coli based on heterologous sigma factors from Bacillus subtilis that recognize specific promoter sequences. Up to four of the analyzed sigma factors can be combined to function orthogonally between each other and toward the host. Additionally, the toolbox is expanded by creating promoter libraries for three sigma factors without loss of their orthogonal nature. As this set covers a wide range of transcription initiation frequencies, it enables tuning of multiple outputs of the circuit in response to different sensory signals in an orthogonal manner. This sigma factor toolbox constitutes an interesting expansion of the synthetic biology toolbox and may contribute to the assembly of more complex synthetic genetic systems in the future.

Bioeconomy for Sustainable Development

Bioeconomy is an emerging paradigm under which the creation, development, and revitalization of economic systems based on a sustainable use of renewable biological resources in a balanced way is rapidly spreading globally. Bioeconomy is building bridges between biotechnology and economy as well as between science, industry, and society. Biotechnology, from its ancient origins up to the present is at the core of the scientific and innovative foundation of bioeconomy policies developed in numerous countries. The challenges and perspectives of bioeconomies are immense, from resource-efficient large-scale manufacturing of products such as chemicals, materials, food, pharmaceuticals, polymers, flavors, and fragrances to the production of new biomaterials and bioenergy in a sustainable and economic way for a growing world population. Key success factors for different countries working on the bioeconomy vary widely from high-tech bioeconomies, emerging diversified or diversified bioeconomies to advanced and basic primary sector bioeconomies. Despite the large variety of bioeconomies, several common elements are identified, which are simultaneously needed altogether.

Developing a codon optimization method for improved expression of recombinant proteins in actinobacteria

Codon optimization by synonymous substitution is widely used for recombinant protein expression. Recent studies have investigated sequence features for codon optimization based on large-scale expression analyses. However, these studies have been limited to common host organisms such as Escherichia coli. Here, we develop a codon optimization method for Rhodococcus erythropolis, a gram-positive GC-rich actinobacterium attracting attention as an alternative host organism. We evaluate the recombinant protein expression of 204 genes in R. erythropolis with the same plasmid vector. The statistical analysis of these expression data reveals that the mRNA folding energy at 5’ regions as well as the codon frequency are important sequence features for codon optimization. Intriguingly, other sequence features such as the codon repetition rate show a different tendency from the previous study on E. coli. We optimize the coding sequences of 12 genes regarding these sequence features, and confirm that 9 of them (75%) achieve increased expression levels compared with wild-type sequences. Especially, for 5 genes whose expression levels for wild-type sequences are small or not detectable, all of them are improved by optimized sequences. These results demonstrate the effectiveness of our codon optimization method in R. erythropolis, and possibly in other actinobacteria.

Identifying the Characteristics of Promising Renewable Replacement Chemicals

Although several “renewable” strategies have been recently proposed to produce high-volume as well as new (replacement) chemicals, the identification of good targets for such strategies remains challenging. Such chemicals that are expensive to obtain today from fossil fuel feedstocks would have an advantage if produced cheaply using alternative methods in the future. In this work we identify the characteristics of such potentially promising replacement chemicals. We also identify the characteristic of promising bio-based replacement chemicals that are relatively easy to obtain through bio-conversions. This work provides insights into the development of renewable chemicals to support a sustainable economy.

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Identified molecular characteristics of potentially promising replacement chemicals

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Key characteristics: number of carbon and oxygen atoms and functional groups

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Proposed metric for evaluating bio-based production efficiency of various chemicals

Engineering Escherichia coli for autoinducible production of L-valine: An example of an artificial positive feedback loop in amino acid biosynthesis

Artificial metabolically regulated inducible expression systems are often used for the production of essential compounds. In most cases, the application of such systems enables regulating the expression of an entire group of genes in response to any internal signal such as an aerobic/anaerobic switch, a transition to stationary phase, or the exhausting of essential compounds. In this work, we demonstrate an example of another type of artificial autoinducible module, denoted a positive feedback module. This positive feedback module generates an inducer molecule that in turn enhances its own synthesis, promoting an activation signal. Due to the use of acetolactate, an intermediate of the L-valine biosynthetic pathway, as a specific inducer molecule, we realized a positive feedback loop in the biosynthetic pathway of branched chain amino acids. Such positive feedback was demonstrated to improve the production of a target compound.

Back to the future: Why we need enzymology to build a synthetic metabolism of the future

Biology is turning from an analytical into a synthetic discipline. This is especially apparent in the field of metabolic engineering, where the concept of synthetic metabolism has been recently developed. Compared to classical metabolic engineering efforts, synthetic metabolism aims at creating novel metabolic networks in a rational fashion from bottom-up. However, while the theoretical design of synthetic metabolic networks has made tremendous progress, the actual realization of such synthetic pathways is still lacking behind. This is mostly because of our limitations in enzyme discovery and engineering to provide the parts required to build synthetic metabolism. Here I discuss the current challenges and limitations in synthetic metabolic engineering and elucidate how modern day enzymology can help to build a synthetic metabolism of the future.

History, Current State, and Emerging Applications of Industrial Biotechnology

The past 150 years have seen remarkable discoveries, rapidly growing biological knowledge, and giant technological leaps providing biotechnological solutions for healthcare, food production, and other societal needs. Genetic engineering, miniaturization, and ever-increasing computing power, in particular, have been key technological drivers for the past few decades. Looking ahead, the eventual transition from fossil resources to biomass and CO₂ demands a shift toward a 'bio-economy' based on novel production processes and engineered organisms.

How adaptive evolution reshapes metabolism to improve fitness: recent advances and future outlook

Adaptive laboratory evolution (ALE) has emerged as a powerful tool in basic microbial research and strain development. In the context of metabolic science and engineering, it has been applied to study gene knockout responses, expand substrate ranges, improve tolerance to process conditions, and to improve productivity via designed growth coupling. In recent years, advancements in ALE methods and systems biology measurement technologies, particularly genome sequencing and ¹³C metabolic flux analysis (¹³C-MFA), have enabled detailed study of the mechanisms and dynamics of evolving metabolism. In this review, we discuss a range of studies that have applied flux analysis to adaptively evolved strains, as well as modeling frameworks developed to predict and interpret evolved fluxes. These efforts link mutations to fitness-enhanced phenotypes, identify bottlenecks and approaches to resolve them, and address systems concepts such as optimality.

A Yeast-Based Biosensor for Screening of Short- and Medium-Chain Fatty Acid Production

Short- and medium-chain fatty acids (SMCFA) are important platform chemicals currently produced from nonsustainable resources. The engineering of microbial cells to produce SMCFA, however, lacks high-throughput methods to screen for best performing cells. Here, we present the development of a whole-cell biosensor for easy and rapid detection of SMCFA. The biosensor is based on a multicopy yeast plasmid containing the SMCFA-responsive PDR12 promoter coupled to GFP as the reporter gene. The sensor detected hexanoic, heptanoic and octanoic acid over a linear range up to 2, 1.5, and 0.75 mM, respectively, but did not show a linear response to decanoic and dodecanoic acid. We validated the functionality of the biosensor with culture supernatants of a previously engineered Saccharomyces cerevisiae octanoic acid producer strain and derivatives thereof. The biosensor signal correlated strongly with the octanoic acid concentrations as determined by gas chromatography. Thus, this biosensor enables the high-throughput screening of SMCFA producers and has the potential to drastically speed up the engineering of diverse SMCFA producing cell factories.

The challenge and potential of photosynthesis: unique considerations for metabolic flux measurements in photosynthetic microorganisms

Photosynthetic microorganisms have the potential for sustainable production of chemical feedstocks and products but have had limited success due to a lack of tools and deeper understanding of metabolic pathway regulation. The application of instationary metabolic flux analysis (INST-MFA) to photosynthetic microorganisms has allowed researchers to quantify fluxes and identify bottlenecks and metabolic inefficiencies to improve strain performance or gain insight into cellular physiology. Additionally, flux measurements can also highlight deviations between measured and predicted fluxes, revealing weaknesses in metabolic models and highlighting areas where a lack of understanding still exists. In this review, we outline the experimental steps necessary to successfully perform photosynthetic flux experiments and analysis. We also discuss the challenges unique to photosynthetic microorganisms and how to account for them, including: light supply, quenching, concentration, extraction, analysis, and flux calculation. We hope that this will enable a larger number of researchers to successfully apply isotope assisted metabolic flux analysis (¹³C-MFA) to their favorite photosynthetic organism.

Modeling population dynamics in a microbial consortium under control of a synthetic pheromone-mediated communication system

Microbial consortia can be used to catalyze complex biotransformations. Tools to control the behavior of these consortia in a technical environment are currently lacking. In the present study, a synthetic biology approach was used to build a model consortium of two Saccharomyces cerevisiae strains where growth and expression of the fluorescent marker protein EGFP by the receiver strain is controlled by the concentration of α-factor pheromone, which is produced by the emitter strain. We have developed a quantitative experimental and theoretical framework to describe population dynamics in the model consortium. We measured biomass growth and metabolite production in controlled bioreactor experiments, and used flow cytometry to monitor changes of the subpopulations and protein expression under different cultivation conditions. This dataset was used to parameterize a segregated mathematical model, which took into account fundamental growth processes, pheromone-induced growth arrest and EGFP production, as well as pheromone desensitization after extended exposure. The model was able to predict the growth dynamics of single-strain cultures and the consortium quantitatively and provides a basis for using this approach in actual biotransformations.

CRISPR-Cas9/Cas12a biotechnology and application in bacteria

CRISPR-Cas technologies have greatly reshaped the biology field. In this review, we discuss the CRISPR-Cas with a particular focus on the associated technologies and applications of CRISPR-Cas9 and CRISPR-Cas12a, which have been most widely studied and used. We discuss the biological mechanisms of CRISPR-Cas as immune defense systems, recently-discovered anti-CRISPR-Cas systems, and the emerging Cas variants (such as xCas9 and Cas13) with unique characteristics. Then, we highlight various CRISPR-Cas biotechnologies, including nuclease-dependent genome editing, CRISPR gene regulation (including CRISPR interference/activation), DNA/RNA base editing, and nucleic acid detection. Last, we summarize up-to-date applications of the biotechnologies for synthetic biology and metabolic engineering in various bacterial species.

Engineering Escherichia coli to increase triacetic acid lactone (TAL) production using an optimized TAL sensor-reporter system

Triacetic acid lactone (TAL) (4-hydroxy-6-methyl-2-pyrone) can be upgraded into a variety of higher-value products, and has potential to be developed into a renewable platform chemical through metabolic engineering. We previously developed an endogenous TAL sensor based on the regulatory protein AraC, and applied it to screen 2-pyrone synthase (2-PS) variant libraries in E. coli, resulting in the identification of variants conferring up to 20-fold improved TAL production in liquid culture. In this study, the sensor-reporter system was further optimized and used to further improve TAL production from recombinant E. coli, this time by screening a genomic overexpression library. We identified new and unpredictable gene targets (betT, ompN, and pykA), whose plasmid-based expression improved TAL yield (mg/L/OD595) up to 49% over the control strain. This work further demonstrates the utility of customized transcription factors as molecular reporters in high-throughput engineering of biocatalytic strains.

Enhancing Coupled Enzymatic Activity by Colocalization on Nanoparticle Surfaces: Kinetic Evidence for Directed Channeling of Intermediates

Multistep enzymatic cascades are becoming more prevalent in industrial settings as engineers strive to synthesize complex products and pharmaceuticals in economical, environmentally friendly ways. Previous work has shown that immobilizing enzymes on nanoparticles can enhance their activity significantly due to localized interfacial effects, and this enhancement remains in place even when that enzyme's activity is coupled to another enzyme that is still freely diffusing. Here, we investigate the effects of displaying two enzymes with coupled catalytic activity directly on the same nanoparticle surface. For this, the well-characterized enzymes pyruvate kinase (PykA) and lactate dehydrogenase (LDH) were utilized as a model system; they jointly convert phosphoenolpyruvate to lactate in two sequential steps as part of downstream glycolysis. The enzymes were expressed with terminal polyhistidine tags to facilitate their conjugation to semiconductor quantum dots (QDs) which were used here as prototypical nanoparticles. Characterization of enzyme coassembly to two different sized QDs showed a propensity to cross-link into nanoclusters consisting of primarily dimers and some trimers. Individual and joint enzyme activity in this format was extensively investigated in direct comparison to control samples lacking the QD scaffolds. We found that QD association enhances LDH activity by >50-fold and its total turnover by at least 41-fold, and that this high activation appears to be largely due to stabilization of its quarternary structure. When both enzymes are simultaneously bound to the QD surfaces, their colocalization leads to >100-fold improvements in the overall rates of coupled activity. Experimental results in conjunction with detailed kinetic simulations provide evidence that this significant improvement in coupled activity is partially attributable to a combination of enhanced enzymatic activity and stabilization of LDH. More importantly, experiments aimed at disrupting channeled processes and further kinetic modeling suggest that the bulk of the performance enhancement arises from intermediary "channeling" between the QD-colocalized enzymes. A full understanding of the underlying processes that give rise to such enhancements from coupled enzymatic activity on nanoparticle scaffolds can provide design criteria for improved biocatalytic applications.

Modular Pathway Rewiring of Yeast for Amino Acid Production

Amino acids find various applications in biotechnology in view of their importance in the food, feed, pharmaceutical, and personal care industries as nutrients, additives, and drugs, respectively. For the large-scale production of amino acids, microbial cell factories are widely used and the development of amino acid-producing strains has mainly focused on prokaryotes Corynebacterium glutamicum and Escherichia coli. However, the eukaryote Saccharomyces cerevisiae is becoming an even more appealing microbial host for production of amino acids and derivatives because of its superior molecular and physiological features, such as amenable to genetic engineering and high tolerance to harsh conditions. To transform S. cerevisiae into an industrial amino acid production platform, the highly coordinated and multiple layers regulation in its amino acid metabolism should be relieved and reconstituted to optimize the metabolic flux toward synthesis of target products. This chapter describes principles, strategies, and applications of modular pathway rewiring in yeast using the engineering of l-ornithine metabolism as a paradigm. Additionally, detailed protocols for in vitro module construction and CRISPR/Cas-mediated pathway assembly are provided.

Analyzing and Designing Cell Factories with OptFlux

OptFlux was launched in 2010 as the first open-source and user-friendly platform containing all the major methods for performing metabolic engineering tasks in silico. Main features included the possibility of performing microbial strain simulations with widely used methods such as Flux Balance Analysis and strain design using Evolutionary Algorithms. Since then, OptFlux suffered a major re-factoring to improve its efficiency and reliability, while many features were added in the form of novel plug-ins, such as the BioVisualizer and the over/under expression plug-ins. The current chapter described the main mathematical formulations of the major methods implemented within OptFlux, also providing a detailed guide on the usage of those functionalities.

Directed Evolution of Protein Catalysts

Directed evolution is a powerful technique for generating tailor-made enzymes for a wide range of biocatalytic applications. Following the principles of natural evolution, iterative cycles of mutagenesis and screening or selection are applied to modify protein properties, enhance catalytic activities, or develop completely new protein catalysts for non-natural chemical transformations. This review briefly surveys the experimental methods used to generate genetic diversity and screen or select for improved enzyme variants. Emphasis is placed on a key challenge, namely how to generate novel catalytic activities that expand the scope of natural reactions. Two particularly effective strategies, exploiting catalytic promiscuity and rational design, are illustrated by representative examples of successfully evolved enzymes. Opportunities for extending these approaches to more complex biocatalytic systems are also considered.

Improving formaldehyde consumption drives methanol assimilation in engineered E. coli

Due to volatile sugar prices, the food vs fuel debate, and recent increases in the supply of natural gas, methanol has emerged as a promising feedstock for the bio-based economy. However, attempts to engineer Escherichia coli to metabolize methanol have achieved limited success. Here, we provide a rigorous systematic analysis of several potential pathway bottlenecks. We show that regeneration of ribulose 5-phosphate in E. coli is insufficient to sustain methanol assimilation, and overcome this by activating the sedoheptulose bisphosphatase variant of the ribulose monophosphate pathway. By leveraging the kinetic isotope effect associated with deuterated methanol as a chemical probe, we further demonstrate that under these conditions overall pathway flux is kinetically limited by methanol dehydrogenase. Finally, we identify NADH as a potent kinetic inhibitor of this enzyme. These results provide direction for future engineering strategies to improve methanol utilization, and underscore the value of chemical biology methodologies in metabolic engineering.

Rediverting carbon flux in Clostridium ljungdahlii using CRISPR interference (CRISPRi)

Clostridium ljungdahlii has emerged as an attractive candidate for the bioconversion of synthesis gas (CO, CO₂, H₂) to a variety of fuels and chemicals through the Wood-Ljungdahl pathway. However, metabolic engineering and pathway elucidation in this microbe is limited by the lack of genetic tools to downregulate target genes. To overcome this obstacle, here we developed an inducible CRISPR interference (CRISPRi) system for C. ljungdahlii that enables efficient (> 94%) transcriptional repression of several target genes, both individually and in tandem. We then applied CRISPRi in a strain engineered for 3-hydroxybutyrate (3HB) production to examine targets for increasing carbon flux toward the desired product. Downregulating phosphotransacetylase (pta) with a single sgRNA led to a 97% decrease in enzyme activity and a 2.3-fold increase in titer during heterotrophic growth. However, acetate production still accounted for 40% of the carbon flux. Repression of aldehyde:ferredoxin oxidoreductase (aor2), another potential route for acetate production, led to a 5% reduction in acetate flux, whereas using an additional sgRNA targeted to pta reduced the enzyme activity to 0.7% of the wild-type level, and further reduced acetate production to 25% of the carbon flux with an accompanying increase in 3HB titer and yield. These results demonstrate the utility of CRISPRi for elucidating and controlling carbon flow in C. ljungdahlii.

Integrating T7 RNA Polymerase and Its Cognate Transcriptional Units for a Host-Independent and Stable Expression System in Single Plasmid

Metabolic engineering and synthetic biology usually require universal expression systems for stable and efficient gene expression in various organisms. In this study, a host-independent and stable T7 expression system had been developed by integrating T7 RNA polymerase and its cognate transcriptional units in single plasmid. The expression of T7 RNA polymerase was restricted below its lethal threshold using a T7 RNA polymerase antisense gene cassette, which allowed long periods of cultivation and protein production. In addition, by designing ribosome binding sites, we further tuned the expression capacity of this novel T7 system within a wide range. This host-independent expression system efficiently expressed genes in five different Gram-negative strains and one Gram-positive strain and was also shown to be applicable in a real industrial d- p-hydroxyphenylglycine production system.

Genetic Optimization Algorithm for Metabolic Engineering Revisited

To date, several independent methods and algorithms exist for exploiting constraint-based stoichiometric models to find metabolic engineering strategies that optimize microbial production performance. Optimization procedures based on metaheuristics facilitate a straightforward adaption and expansion of engineering objectives, as well as fitness functions, while being particularly suited for solving problems of high complexity. With the increasing interest in multi-scale models and a need for solving advanced engineering problems, we strive to advance genetic algorithms, which stand out due to their intuitive optimization principles and the proven usefulness in this field of research. A drawback of genetic algorithms is that premature convergence to sub-optimal solutions easily occurs if the optimization parameters are not adapted to the specific problem. Here, we conducted comprehensive parameter sensitivity analyses to study their impact on finding optimal strain designs. We further demonstrate the capability of genetic algorithms to simultaneously handle (i) multiple, non-linear engineering objectives; (ii) the identification of gene target-sets according to logical gene-protein-reaction associations; (iii) minimization of the number of network perturbations; and (iv) the insertion of non-native reactions, while employing genome-scale metabolic models. This framework adds a level of sophistication in terms of strain design robustness, which is exemplarily tested on succinate overproduction in Escherichia coli.

Optogenetic regulation of engineered cellular metabolism for microbial chemical production

The optimization of engineered metabolic pathways requires careful control over the levels and timing of metabolic enzyme expression^1-4. Optogenetic tools are ideal for achieving such precise control, as light can be applied and removed instantly without complex media changes. Here we show that light-controlled transcription can be used to enhance the biosynthesis of valuable products in engineered Saccharomyces cerevisiae. We introduce new optogenetic circuits to shift cells from a light-induced growth phase to a darkness-induced production phase, which allows us to control fermentation purely with light. Furthermore, optogenetic control of engineered pathways enables a new mode of bioreactor operation using periodic light pulses to tune enzyme expression during the production phase of fermentation to increase yields. Using these advances, we control the mitochondrial isobutanol pathway to produce up to 8.49 ± 0.31 g/L of isobutanol and 2.38 ± 0.06 g/L of 2-methyl-1-butanol micro-aerobically from glucose. These results make a compelling case for the application of optogenetics to metabolic engineering for valuable products.

Microbial Cell Factories for the Production of Terpenoid Flavor and Fragrance Compounds

Terpenoid flavor and fragrance compounds are of high interest to the aroma industry. Microbial production offers an alternative sustainable access to the desired terpenoids independent of natural sources. Genetically engineered microorganisms can be used to synthesize terpenoids from cheap and renewable resources. Due to its modular architecture, terpenoid biosynthesis is especially well suited for the microbial cell factory concept: a platform host engineered for a high flux toward the central C₅ prenyl diphosphate precursors enables the production of a broad range of target terpenoids just by varying the pathway modules converting the C₅ intermediates to the product of interest. In this review typical terpenoid flavor and fragrance compounds marketed or under development by biotech and aroma companies are given, and the specificities of the aroma market are discussed. The main part of this work focuses on key strategies and recent advances to engineer microbes to become efficient terpenoid producers.

Directed evolution of the 3-hydroxypropionic acid production pathway by engineering aldehyde dehydrogenase using a synthetic selection device

3-Hydroxypropionic acid (3-HP) is an important platform chemical, and biological production of 3-HP from glycerol as a carbon source using glycerol dehydratase (GDHt) and aldehyde dehydrogenase (ALDH) has been revealed to be effective because it involves a relatively simple metabolic pathway and exhibits higher yield and productivity than other biosynthetic pathways. Despite the successful attempts of 3-HP production from glycerol, the biological process suffers from problems arising from low activity and inactivation of the two enzymes. To apply the directed evolutionary approach to engineer the 3-HP production system, we constructed a synthetic selection device using a 3-HP-responsive transcription factor and developed a selection approach for screening 3-HP-producing microorganisms. The method was applied to an ALDH library, specifically aldehyde-binding site library of alpha-ketoglutaric semialdehyde dehydrogenase (KGSADH). Only two serial cultures resulted in enrichment of strains showing increased 3-HP production, and an isolated KGSADH variant enzyme exhibited a 2.79-fold higher catalytic efficiency toward its aldehyde substrate than the wild-type one. This approach will provide the simple and efficient tool to engineer the pathway enzymes in metabolic engineering.

Pursuing the Promise of Enzymatic Enhancement with Nanoparticle Assemblies

The growing emphasis on green chemistry, renewable resources, synthetic biology, regio-/stereospecific chemical transformations, and nanotechnology for providing new biological products and therapeutics is reinvigorating research into enzymatic catalysis. Although the promise is profound, many complex issues remain to be addressed before this effort will have a significant impact. Prime among these is to combat the degradation of enzymes frequently seen in ex vivo formats following immobilization to stabilize the enzymes for long-term application and to find ways of enhancing their activity. One promising avenue for progress on these issues is via nanoparticle (NP) display, which has been found in a number of cases to enhance enzyme activity while also improving long-term stability. In this feature article, we discuss the phenomenon of enhanced enzymatic activity at NP interfaces with an emphasis on our own work in this area. Important factors such as NP surface chemistry, bioconjugation approaches, and assay formats are first discussed because they can critically affect the observed enhancement. Examples are given of improved performance for enzymes such as phosphotriesterase, alkaline phosphatase, trypsin, horseradish peroxidase, and β-galactosidase and in configurations with either the enzyme or the substrate attached to the NP. The putative mechanisms that give rise to the performance boost are discussed along with how detailed kinetic modeling can contribute to their understanding. Given the importance of biosensing, we also highlight how this configuration is already making a significant contribution to NP-based enzymatic sensors. Finally, a perspective is provided on how this field may develop and how NP-based enzymatic enhancement can be extended to coupled systems and multienzyme cascades.