Systems strategies for developing industrial microbial strains

Carboxylation of Hydroxyaromatic Compounds with HCO3− by Enzyme Catalysis: Recent Advances Open the Perspective for Valorization of Lignin-Derived Aromatics

This review focuses on recent advances in the field of enzymatic carboxylation reactions of hydroxyaromatic compounds using HCO3− (as a CO2 source) to produce hydroxybenzoic and other phenolic acids in mild conditions with high selectivity and moderate to excellent yield. Nature offers an extensive portfolio of enzymes catalysing reversible decarboxylation of hydroxyaromatic acids, whose equilibrium can be pushed towards the side of the carboxylated products. Extensive structural and mutagenesis studies have allowed recent advances in the understanding of the reaction mechanism of decarboxylase enzymes, ultimately enabling an improved yield and expansion of the scope of the reaction. The topic is of particular relevance today as the scope of the carboxylation reactions can be extended to include lignin-related compounds in view of developing lignin biorefinery technology.

In silico identification of metabolic engineering strategies for improved lipid production in Yarrowia lipolytica by genome-scale metabolic modeling

BACKGROUND

Yarrowia lipolytica, an oleaginous yeast, is a promising platform strain for production of biofuels and oleochemicals as it can accumulate a high level of lipids in response to nitrogen limitation. Accordingly, many metabolic engineering efforts have been made to develop engineered strains of Y. lipolytica with higher lipid yields. Genome-scale model of metabolism (GEM) is a powerful tool for identifying novel genetic designs for metabolic engineering. Several GEMs for Y. lipolytica have recently been developed; however, not many applications of the GEMs have been reported for actual metabolic engineering of Y. lipolytica. The major obstacle impeding the application of Y. lipolytica GEMs is the lack of proper methods for predicting phenotypes of the cells in the nitrogen-limited condition, or more specifically in the stationary phase of a batch culture.

RESULTS

In this study, we showed that environmental version of minimization of metabolic adjustment (eMOMA) can be used for predicting metabolic flux distribution of Y. lipolytica under the nitrogen-limited condition and identifying metabolic engineering strategies to improve lipid production in Y. lipolytica. Several well-characterized overexpression targets, such as diglyceride acyltransferase, acetyl-CoA carboxylase, and stearoyl-CoA desaturase, were successfully rediscovered by our eMOMA-based design method, showing the relevance of prediction results. Interestingly, the eMOMA-based design method also suggested non-intuitive knockout targets, and we experimentally validated the prediction with a mutant lacking YALI0F30745g, one of the predicted targets involved in one-carbon/methionine metabolism. The mutant accumulated 45% more lipids compared to the wild-type.

CONCLUSION

This study demonstrated that eMOMA is a powerful computational method for understanding and engineering the metabolism of Y. lipolytica and potentially other oleaginous microorganisms.

An Optimized Bistable Metabolic Switch To Decouple Phenotypic States during Anaerobic Fermentation

Metabolic engineers aim to genetically modify microorganisms to improve their ability to produce valuable compounds. Despite the prevalence of growth-coupled production processes, these strategies can significantly limit production rates. Instead, rates can be improved by decoupling and optimizing growth and production independently, and operating with a growth stage followed by a production stage. Here, we implement a bistable transcriptional controller to decouple and switch between these two states. We optimize the controller in anaerobic conditions, typical of industrial fermentations, to ensure stability and tight expression control, while improving switching dynamics. The stability of this controller can be maintained through a simulated seed train scale-up from 5 mL to 500 000 L, indicating industrial feasibility. Finally, we demonstrate a two-stage production process using our optimal construct to improve the instantaneous rate of lactate production by over 50%, motivating the use of these systems in broad metabolic engineering applications.

aMSGE: advanced multiplex site-specific genome engineering with orthogonal modular recombinases in actinomycetes

Chromosomal integration of genes and pathways is of particular importance for large-scale and long-term fermentation in industrial biotechnology. However, stable, multi-copy integration of long DNA segments (e.g., large gene clusters) remains challenging. Here, we describe a plug-and-play toolkit that allows for high-efficiency, single-step, multi-locus integration of natural product (NP) biosynthetic gene clusters (BGCs) in actinomycetes, based on the innovative concept of "multiple integrases-multiple attB sites". This toolkit consists of 27 synthetic modular plasmids, which contain single- or multi-integration modules (from two to four) derived from five orthogonal site-specific recombination (SSR) systems. The multi-integration modules can be readily ligated into plasmids containing large BGCs by Gibson assembly, which can be simultaneously inserted into multiple native attB sites in a single step. We demonstrated the applicability of this toolkit by performing stabilized amplification of acetyl-CoA carboxylase genes to facilitate actinorhodin biosynthesis in Streptomyces coelicolor. Furthermore, using this toolkit, we achieved a 185.6% increase in 5-oxomilbemycin titers (from 2.23 to 6.37 g/L) in Streptomyces hygroscopicus via the multi-locus integration of the entire 5-oxomilbemycin BGC (72 kb) (up to four copies). Compared with previously reported methods, the advanced multiplex site-specific genome engineering (aMSGE) method does not require the introduction of any modifications into host genomes before the amplification of target genes or BGCs, which will drastically simplify and accelerate efforts to improve NP production. Considering that SSR systems are widely distributed in a variety of industrial microbes, this novel technique also promises to be a valuable tool for the enhanced biosynthesis of other high-value bioproducts.

Identification of growth-coupled production strains considering protein costs and kinetic variability

Conversion of renewable biomass to useful molecules in microbial cell factories can be approached in a rational and systematic manner using constraint-based reconstruction and analysis. Filtering for high confidence in silico designs is critical because in vivo construction and testing of strains is expensive and time consuming. As such, a workflow was devised to analyze the robustness of growth-coupled production when considering the biosynthetic costs of the proteome and variability in enzyme kinetic parameters using a genome-scale model of metabolism and gene expression (ME-model). A collection of 2632 unfiltered knockout designs in Escherichia coli was evaluated by the workflow. A ME-model was used in the workflow to test the designs' growth-coupled production in addition to a less complex genome-scale metabolic model (M-model). The workflow identified 634 M-model growth-coupled designs which met the filtering criteria and 42 robust designs, which met growth-coupled production criteria using both M and ME-models. Knockouts were found to follow a pattern of controlling intermediate metabolite consumption such as pyruvate consumption and high flux subsystems such as glycolysis. Kinetic parameter sampling using the ME-model revealed how enzyme efficiency and pathway tradeoffs can affect growth-coupled production phenotypes.

Regulatory non-coding sRNAs in bacterial metabolic pathway engineering

Non-coding RNAs (ncRNAs) are versatile and powerful controllers of gene expression that have been increasingly linked to cellular metabolism and phenotype. In bacteria, identified and characterized ncRNAs range from trans-acting, multi-target small non-coding RNAs to dynamic, cis-encoded regulatory untranslated regions and riboswitches. These native regulators have inspired the design and construction of many synthetic RNA devices. In this work, we review the design, characterization, and impact of ncRNAs in engineering both native and exogenous metabolic pathways in bacteria. We also consider the opportunities afforded by recent high-throughput approaches for characterizing sRNA regulators and their corresponding networks to showcase their potential applications and impact in engineering bacterial metabolism.

Deep scanning lysine metabolism in Escherichia coli

Our limited ability to predict genotype-phenotype relationships has called for strategies that allow testing of thousands of hypotheses in parallel. Deep scanning mutagenesis has been successfully implemented to map genotype-phenotype relationships at a single-protein scale, allowing scientists to elucidate properties that are difficult to predict. However, most phenotypes are dictated by several proteins that are interconnected through complex and robust regulatory and metabolic networks. These sophisticated networks hinder our understanding of the phenotype of interest and limit our capabilities to rewire cellular functions. Here, we leveraged CRISPR-EnAbled Trackable genome Engineering to attempt a parallel and high-resolution interrogation of complex networks, deep scanning multiple proteins associated with lysine metabolism in Escherichia coli We designed over 16,000 mutations to perturb this pathway and mapped their contribution toward resistance to an amino acid analog. By doing so, we identified different routes that can alter pathway function and flux, uncovering mechanisms that would be difficult to rationally design. This approach sets a framework for forward investigation of complex multigenic phenotypes.

Systematic Analysis of Bottlenecks in a Multibranched and Multilevel Regulated Pathway: The Molecular Fundamentals of l-Methionine Biosynthesis in Escherichia coli

To produce chemicals and fuels from renewable resources, various strategies and genetic tools have been developed to redesign pathways and optimize the metabolic flux in microorganisms. However, in most successful cases, the target chemicals are synthesized through a linear pathway, and regular methodologies for the identification of bottlenecks and metabolic flux optimization in multibranched and multilevel regulated pathways, such as the l-methionine biosynthetic pathway, have rarely been reported. In the present study, a systematic analysis strategy was employed to gradually reveal and remove the potential bottlenecks limiting the l-methionine biosynthesis in E. coli. 80 genes in central metabolism and selected amino acids biosynthetic pathways were first repressed or upregulated to probe their effects on l-methionine accumulation. The l-methionine biosynthetic pathway was then modularized and iteratively genetic modifications were performed to uncover the multiple layers of limitations and stepwise improve the l-methionine titer. The metabolomics data further revealed a more evenly distributed metabolic flux in l-methionine biosynthesis pathway of the optimal strain and provided valuable suggestions for further optimization. The optimal strain produced 16.86 g/L of l-methionine in 48 h by fed-batch fermentation. This work is the first to our knowledge to systematically elucidate the molecular fundamentals of multilevel regulation of l-methionine biosynthesis. It also demonstrated that the systematic analysis strategy can boost our ability to identify the potential bottlenecks and optimize the metabolic flux in multibranched and multilevel regulated pathways for the production of corresponding chemicals.

Metabolic flux balance analysis during lactate and glucose concomitant consumption in HEK293 cell cultures

At early stages of the exponential growth phase in HEK293 cell cultures, the tricarboxylic acid cycle is unable to process all the amount of NADH generated in the glycolysis pathway, being lactate the main by-product. However, HEK293 cells are also able to metabolize lactate depending on the environmental conditions. It has been recently observed that one of the most important modes of lactate metabolization is the cometabolism of lactate and glucose, observed even during the exponential growth phase. Extracellular lactate concentration and pH appear to be the key factors triggering the metabolic shift from glucose consumption and lactate production to lactate and glucose concomitant consumption. The hypothesis proposed for triggering this metabolic shift to lactate and glucose concomitant consumption is that HEK293 cells metabolize extracellular lactate as a response to both extracellular protons and lactate accumulation, by means of cotransporting them (extracellular protons and lactate) into the cytosol. At this point, there exists a considerable controversy about how lactate reaches the mitochondrial matrix: the first hypothesis proposes that lactate is converted into pyruvate in the cytosol, and afterward, pyruvate enters into the mitochondria; the second alternative considers that lactate enters first into the mitochondria, and then, is converted into pyruvate. In this study, lactate transport and metabolization into mitochondria is shown to be feasible, as evidenced by means of respirometry tests with isolated active mitochondria, including the depletion of lactate concentration of the respirometry assay. Although the capability of lactate metabolization by isolated mitochondria is demonstrated, the possibility of lactate being converted into pyruvate in the cytosol cannot be excluded from the discussion. For this reason, the calculation of the metabolic fluxes for an HEK293 cell line was performed for the different metabolic phases observed in batch cultures under pH controlled and noncontrolled conditions, considering both hypotheses. The main objective of this study is to evaluate the redistribution of cellular metabolism and compare the differences or similarities between the phases before and after the metabolic shift of HEK293 cells (shift observed when pH is not controlled). That is from a glucose consumption/lactate production phase to a glucose-lactate coconsumption phase. Interestingly, switching to a glucose and lactate cometabolization results in a better-balanced cell metabolism, with decreased glucose and amino acids uptake rates, affecting minimally cell growth. This behavior could be applied to further develop new approaches in terms of cell engineering and to develop improved cell culture strategies in the field of animal cell technology.

Dual function of ammonium acetate in acetone-butanol-ethanol fermentation by Clostridium acetobutylicum

In this study, a compound nitrogen source, integrating the advantages of ammonium acetate and soybean meal, was proposed to further improve acetone-butanol-ethanol fermentation. Unfortunately, this compound nitrogen source was found to effectively inhibit cellular performance, as the introduction of NH₄⁺ significantly decreased the yield of butanol and total solvents by 34.78% and 35.14%, to only 6.62 g/L and 10.76 g/L, respectively. Meanwhile, the regulatory mechanism was further elucidated at different levels. As a result, the NH₄⁺ could down-regulate the transcriptional levels of key genes involved in butanol synthesis, and the activity of acetoacetyl-CoA/acyl-CoA transferase, and then decrease the accumulation of key intermediates. Therefore, ammonium acetate has a dual function in ABE fermentation, as it effectively improves ABE fermentation when it is the sole nitrogen source but significantly decreases fermentation performance in the presence of soybean meal, broadening the understanding of nitrogen regulation mechanism of C. acetobutylicum.

In silico model-guided identification of transcriptional regulator targets for efficient strain design

BACKGROUND

Cellular metabolism is tightly regulated by hard-wired multiple layers of biological processes to achieve robust and homeostatic states given the limited resources. As a result, even the most intuitive enzyme-centric metabolic engineering endeavours through the up-/down-regulation of multiple genes in biochemical pathways often deliver insignificant improvements in the product yield. In this regard, targeted engineering of transcriptional regulators (TRs) that control several metabolic functions in modular patterns is an interesting strategy. However, only a handful of in silico model-added techniques are available for identifying the TR manipulation candidates, thus limiting its strain design application.

RESULTS

We developed hierarchical-Beneficial Regulatory Targeting (h-BeReTa) which employs a genome-scale metabolic model and transcriptional regulatory network (TRN) to identify the relevant TR targets suitable for strain improvement. We then applied this method to industrially relevant metabolites and cell factory hosts, Escherichia coli and Corynebacterium glutamicum. h-BeReTa suggested several promising TR targets, many of which have been validated through literature evidences. h-BeReTa considers the hierarchy of TRs in the TRN and also accounts for alternative metabolic pathways which may divert flux away from the product while identifying suitable metabolic fluxes, thereby performing superior in terms of global TR target identification.

CONCLUSIONS

In silico model-guided strain design framework, h-BeReTa, was presented for identifying transcriptional regulator targets. Its efficacy and applicability to microbial cell factories were successfully demonstrated via case studies involving two cell factory hosts, as such suggesting several intuitive targets for overproducing various value-added compounds.

Synthetic redesign of central carbon and redox metabolism for high yield production of N-acetylglucosamine in Bacillus subtilis

One of the primary goals of microbial metabolic engineering is to achieve high titer, yield and productivity (TYP) of engineered strains. This TYP index requires optimized carbon flux toward desired molecule with minimal by-product formation. De novo redesign of central carbon and redox metabolism holds great promise to alleviate pathway bottleneck and improve carbon and energy utilization efficiency. The engineered strain, with the overexpression or deletion of multiple genes, typically can't meet the TYP index, due to overflow of central carbon and redox metabolism that compromise the final yield, despite a high titer or productivity might be achieved. To solve this challenge, we reprogramed the central carbon and redox metabolism of Bacillus subtilis and achieved high TYP production of N-acetylglucosamine. Specifically, a "push-pull-promote" approach efficiently reduced the overflown acetyl-CoA flux and eliminated byproduct formation. Four synthetic NAD(P)-independent metabolic routes were introduced to rewire the redox metabolism to minimize energy loss. Implementation of these genetic strategies led us to obtain a B. subtilis strain with superior TYP index. GlcNAc titer in shake flask was increased from 6.6 g L^-1 to 24.5 g L^-1, the yield was improved from 0.115 to 0.468 g GlcNAc g^-1 glucose, and the productivity was increased from 0.274 to 0.437 g L^-1 h^-1. These titer and yield are the highest levels ever reported and, the yield reached 98% of the theoretical pathway yield (0.478 g g^-1 glucose). The synthetic redesign of carbon metabolism and redox metabolism represent a novel and general metabolic engineering strategy to improve the performance of microbial cell factories.

Metabolic Engineering of Escherichia coli for Efficient Production of 2-Pyrone-4,6-dicarboxylic Acid from Glucose

2-Pyrone-4,6-dicarboxylic acid (PDC) is a pseudoaromatic dicarboxylic acid and is a promising biobased building block chemical that can be used to make diverse polyesters with novel functionalities. In this study, Escherichia coli was metabolically engineered to produce PDC from glucose. First, an efficient biosynthetic pathway for PDC production from glucose was suggested by in silico metabolic flux simulation. This best pathway employs a single-step biosynthetic route to protocatechuic acid (PCA), a metabolic precursor for PDC biosynthesis. On the basis of the selected PDC biosynthetic pathway, a shikimate dehydrogenase (encoded by aroE)-deficient E. coli strain was engineered by introducing heterologous genes of different microbial origin encoding enzymes responsible for converting 3-dehydroshikimate (DHS) to PDC, which allowed de novo biosynthesis of PDC from glucose. Next, production of PDC was further improved by applying stepwise rational metabolic engineering strategies. These include elimination of feedback inhibition on 3-deoxy-d-arabino-heptulosonate-7-phosphate synthase (encoded by aroG) by overexpressing a feedback-resistant variant, enhancement of the precursor phosphoenolpyruvate supply by changing the native promoter of the ppsA gene with the strong trc promoter, and reducing accumulation of the major byproduct DHS by overexpression of a DHS importer (encoded by shiA). Furthermore, cofactor (NADP⁺/NADPH) utilization was manipulated through genetic modifications of the E. coli soluble pyridine nucleotide transhydrogenase (encoded by sthA), and the resultant impact on PDC production was investigated. Fed-batch fermentation of the final engineered E. coli strain allowed production of 16.72 g/L of PDC from glucose with the yield and productivity of 0.201 g/g and 0.172 g/L/h, respectively, representing the highest PDC production performance indices reported to date.

Repurposing type III polyketide synthase as a malonyl-CoA biosensor for metabolic engineering in bacteria

Malonyl-CoA is an important central metabolite for the production of diverse valuable chemicals including natural products, but its intracellular availability is often limited due to the competition with essential cellular metabolism. Several malonyl-CoA biosensors have been developed for high-throughput screening of targets increasing the malonyl-CoA pool. However, they are limited for use only in Escherichia coli and Saccharomyces cerevisiae and require multiple signal transduction steps. Here we report development of a colorimetric malonyl-CoA biosensor applicable in three industrially important bacteria: E. coli, Pseudomonas putida, and Corynebacterium glutamicum RppA, a type III polyketide synthase producing red-colored flaviolin, was repurposed as a malonyl-CoA biosensor in E. coli Strains with enhanced malonyl-CoA accumulation were identifiable by the colorimetric screening of cells showing increased red color. Other type III polyketide synthases could also be repurposed as malonyl-CoA biosensors. For target screening, a 1,858 synthetic small regulatory RNA library was constructed and applied to find 14 knockdown gene targets that generally enhanced malonyl-CoA level in E. coli These knockdown targets were applied to produce two polyketide (6-methylsalicylic acid and aloesone) and two phenylpropanoid (resveratrol and naringenin) compounds. Knocking down these genes alone or in combination, and also in multiple different E. coli strains for two polyketide cases, allowed rapid development of engineered strains capable of enhanced production of 6-methylsalicylic acid, aloesone, resveratrol, and naringenin to 440.3, 30.9, 51.8, and 103.8 mg/L, respectively. The malonyl-CoA biosensor developed here is a simple tool generally applicable to metabolic engineering of microorganisms to achieve enhanced production of malonyl-CoA-derived chemicals.

Jungle Express is a versatile repressor system for tight transcriptional control

Tightly regulated promoters are essential for numerous biological applications, where strong inducibility, portability, and scalability are desirable. Current systems are often incompatible with large-scale fermentations due to high inducer costs and strict media requirements. Here, we describe the bottom-up engineering of 'Jungle Express', an expression system that enables efficient gene regulation in diverse proteobacteria. This system is guided by EilR, a multidrug-binding repressor with high affinity to its optimized operator and cationic dyes that act as powerful inducers at negligible costs. In E. coli, the engineered promoters exhibit minimal basal transcription and are inducible over four orders of magnitude by 1 µM crystal violet, reaching expression levels exceeding those of the strongest current bacterial systems. Further, we provide molecular insights into specific interactions of EilR with its operator and with two inducers. The versatility of Jungle Express opens the way for tightly controlled and efficient gene expression that is not restricted to host organism, substrate, or scale.

A synthetic medium to simulate sugarcane molasses

BACKGROUND

Developing novel microbial cell factories requires careful testing of candidates under industrially relevant conditions. However, this frequently occurs late during the strain development process. The availability of laboratory media that simulate industrial-like conditions might improve cell factory development, as they allow for strain construction and testing in the laboratory under more relevant conditions. While sugarcane molasses is one of the most important substrates for the production of biofuels and other bioprocess-based commodities, there are no defined media that faithfully simulate it. In this study, we tested the performance of a new synthetic medium simulating sugarcane molasses.

RESULTS

Laboratory scale simulations of the Brazilian ethanol production process, using both sugarcane molasses and our synthetic molasses (SM), demonstrated good reproducibility of the fermentation performance, using yeast strains, PE-2 and Ethanol Red™. After 4 cycles of fermentation, the final ethanol yield (g_p g_s^-1) values for the SM ranged from 0.43 ± 0.01 to 0.44 ± 0.01 and from 0.40 ± 0.01 to 0.46 ± 0.01 for the molasses-based fermentations. The other fermentation parameters (i.e., biomass production, yeast viability, and glycerol and acetic acid yield) were also within similar value ranges for all the fermentations. Sequential pairwise competition experiments, comparing industrial and laboratory yeast strains, demonstrated the impact of the media on strain fitness. After two sequential cocultivations, the relative abundance of the laboratory yeast strain was 5-fold lower in the SM compared to the yeast extract-peptone-dextrose medium, highlighting the importance of the media composition on strain fitness.

CONCLUSIONS

Simulating industrial conditions at laboratory scale is a key part of the efficient development of novel microbial cell factories. In this study, we have developed a synthetic medium that simulated industrial sugarcane molasses media. We found good agreement between the synthetic medium and the industrial media in terms of the physiological parameters of the industrial-like fermentations.

Advances in bio-based production of dicarboxylic acids longer than C4

Growing concerns of environmental pollution and fossil resource shortage are major driving forces for bio-based production of chemicals traditionally from petrochemical industry. Dicarboxylic acids (DCAs) are important platform chemicals with large market and wide applications, and here the recent advances in bio-based production of straight-chain DCAs longer than C4 from biological approaches, especially by synthetic biology, are reviewed. A couple of pathways were recently designed and demonstrated for producing DCAs, even those ranging from C5 to C15, by employing respective starting units, extending units, and appropriate enzymes. Furthermore, in order to achieve higher production of DCAs, enormous efforts were made in engineering microbial hosts that harbored the biosynthetic pathways and in improving properties of biocatalytic elements to enhance metabolic fluxes toward target DCAs. Here we summarize and discuss the current advantages and limitations of related pathways, and also provide perspectives on synthetic pathway design and optimization for hyper-production of DCAs.

Bioproduction of Benzylamine from Renewable Feedstocks via a Nine-Step Artificial Enzyme Cascade and Engineered Metabolic Pathways

Production of chemicals from renewable feedstocks has been an important task for sustainable chemical industry. Although microbial fermentation has been widely employed to produce many biochemicals, it is still very challenging to access non-natural chemicals. Two methods (biotransformation and fermentation) have been developed for the first bio-derived synthesis of benzylamine, a commodity non-natural amine with broad applications. Firstly, a nine-step artificial enzyme cascade was designed by biocatalytic retrosynthetic analysis and engineered in recombinant E. coli LZ243. Biotransformation of l-phenylalanine (60 mm) with the E. coli cells produced benzylamine (42 mm) in 70 % conversion. Importantly, the cascade biotransformation was scaled up to 100 mL and benzylamine was successfully isolated in 57 % yield. Secondly, an artificial biosynthesis pathway to benzylamine from glucose was developed by combining the nine-step cascade with an enhanced l-phenylalanine synthesis pathway in cells. Fermentation with E. coli LZ249 gave benzylamine in 4.3 mm concentration from glucose. In addition, one-pot syntheses of several useful benzylamines from the easily available styrenes were achieved, representing a new type of alkene transformation by formal oxidative cleavage and reductive amination.

Metabolomics for industrial fermentation

Metabolomics is essential to understand the metabolism and identify engineering targets to improve the performances of strains and bioprocesses. Although numerous metabolomics techniques have been developed and applied to various organisms, the metabolome of Saccharopolyspora erythraea, a native producer of erythromycin, had never been studied. The 2017 best paper of Bioprocess and Biosystems Engineering reports examination of three methods for quenching and extraction to analyze the intracellular metabolome of S. erythraea, and identified the most reliable methods for studying different groups of the metabolites. Subsequent studies on the dynamics of the intracellular metabolome of S. erythraea during the fed-batch fermentation identified a positive correlation between the specific erythromycin production rate and the pool size of intracellular propionyl-CoA and other precursors of erythromycin. A series of follow-up studies, such as demonstrating the applicability of the quenching/extraction methods in other related antibiotic producers, demonstrating the generality of the best matches between the quenching/extraction methods and the metabolite groups, and combining metabolomics approaches with the fluxomics and systems metabolic engineering approaches, will facilitate the metabolomics studies on important antibiotic producers, enable standardization of the quenching/extraction protocols, and improve the performance of the antibiotic production with deeper insight into their metabolism.

Rapid host strain improvement by in vivo rearrangement of a synthetic yeast chromosome

Synthetic biology tools, such as modular parts and combinatorial DNA assembly, are routinely used to optimise the productivity of heterologous metabolic pathways for biosynthesis or substrate utilisation, yet it is well established that host strain background is just as important for determining productivity. Here we report that in vivo combinatorial genomic rearrangement of Saccharomyces cerevisiae yeast with a synthetic chromosome V can rapidly generate new, improved host strains with genetic backgrounds favourable to diverse heterologous pathways, including those for violacein and penicillin biosynthesis and for xylose utilisation. We show how the modular rearrangement of synthetic chromosomes by SCRaMbLE can be easily determined using long-read nanopore sequencing and we explore experimental conditions that optimise diversification and screening. This synthetic genome approach to metabolic engineering provides productivity improvements in a fast, simple and accessible way, making it a valuable addition to existing strain improvement techniques.

The Sc2.0 project has built the Synthetic Chromosome Rearrangement and Modification by LoxP-mediated Evolution (SCRaMbLE) system into their synthetic chromosomes. Here the authors use SCRaMbLE to rapidly develop, diversify and screen strains for diverse production and growth characteristics.

An Oil-Free Picodrop Bioassay Platform for Synthetic Biology

Droplet microfluidics enables massively-parallel analysis of single cells, biomolecules, and chemicals, making it valuable for high-throughput screens. However, many hydrophobic analytes are soluble in carrier oils, preventing their quantitative analysis with the method. We apply Printed Droplet Microfluidics to construct defined reactions with chemicals and cells incubated under air on an open array. The method interfaces with most bioanalytical tools and retains hydrophobic compounds in compartmentalized reactors, allowing their quantitation.

Protein Acylation Affects the Artificial Biosynthetic Pathway for Pinosylvin Production in Engineered E. coli

The effect of regulatory system on the engineered biosynthetic pathway in chassis cells remains incompletely understood in microorganisms. Acyl-CoAs function as key precursors for the biosynthesis of various natural products and the dominant donors for protein acylation. The polyphenol pinosylvin, with high antimicrobial and antifungal activities, is biosynthesized with malonyl-CoA as its direct precursors. But correlation between lysine malonylation and pinosylvin biosynthesis remains unknown. Herein, we found that the malonyl-CoA-driven lysine malonylation plays an important role in interaction between the engineered pathway of pinosylvin synthesis and E. coli chassis cell. Oversupply of malonyl-CoA leads to an increase in malonylation level of global proteome as well as the enzymes in the artificial pathway, thereby decreasing yield of pinosylvin. The results revealed that the intricate balance of cellular acyl-CoA concentrations is critical for the yields of acyl-CoA-derived natural products. We next modified the enzymes in the biosynthetic pathway to adjust their acylation level and successfully improved the yield of pinosylvin. Our study uncovers the effect of protein acylation on the biosynthetic pathway, helps optimization of synthetic constructs, and provides new strategies in metabolic engineering and synthetic biology at the protein post-translational level.

Markerless gene knockout and integration to express heterologous biosynthetic gene clusters in Pseudomonas putida

Pseudomonas putida has gained much interest among metabolic engineers as a workhorse for producing valuable natural products. While a few gene knockout tools for P. putida have been reported, integration of heterologous genes into the chromosome of P. putida, an essential strategy to develop stable industrial strains producing heterologous bioproducts, requires development of a more efficient method. Current methods rely on time-consuming homologous recombination techniques and transposon-mediated random insertions. Here we report a RecET recombineering system for markerless integration of heterologous genes into the P. putida chromosome. The efficiency and capacity of the recombineering system were first demonstrated by knocking out various genetic loci on the P. putida chromosome with knockout lengths widely spanning 0.6-101.7 kb. The RecET recombineering system developed here allowed successful integration of biosynthetic gene clusters for four proof-of-concept bioproducts, including protein, polyketide, isoprenoid, and amino acid derivative, into the target genetic locus of P. putida chromosome. The markerless recombineering system was completed by combining Cre/lox system and developing efficient plasmid curing systems, generating final strains free of antibiotic markers and plasmids. This markerless recombineering system for efficient gene knockout and integration will expedite metabolic engineering of P. putida, a bacterial host strain of increasing academic and industrial interest.

Optimal bioprocess design through a gene regulatory network - Growth kinetic hybrid model: Towards replacing Monod kinetics

Currently, design and optimisation of biotechnological bioprocesses is performed either through exhaustive experimentation and/or with the use of empirical, unstructured growth kinetics models. Whereas, elaborate systems biology approaches have been recently explored, mixed-substrate utilisation is predominantly ignored despite its significance in enhancing bioprocess performance. Herein, bioprocess optimisation for an industrially-relevant bioremediation process involving a mixture of highly toxic substrates, m-xylene and toluene, was achieved through application of a novel experimental-modelling gene regulatory network - growth kinetic (GRN-GK) hybrid framework. The GRN model described the TOL and ortho-cleavage pathways in Pseudomonas putida mt-2 and captured the transcriptional kinetics expression patterns of the promoters. The GRN model informed the formulation of the growth kinetics model replacing the empirical and unstructured Monod kinetics. The GRN-GK framework's predictive capability and potential as a systematic optimal bioprocess design tool, was demonstrated by effectively predicting bioprocess performance, which was in agreement with experimental values, when compared to four commonly used models that deviated significantly from the experimental values. Significantly, a fed-batch biodegradation process was designed and optimised through the model-based control of TOL Pr promoter expression resulting in 61% and 60% enhanced pollutant removal and biomass formation, respectively, compared to the batch process. This provides strong evidence of model-based bioprocess optimisation at the gene level, rendering the GRN-GK framework as a novel and applicable approach to optimal bioprocess design. Finally, model analysis using global sensitivity analysis (GSA) suggests an alternative, systematic approach for model-driven strain modification for synthetic biology and metabolic engineering applications.

Cameo: A Python Library for Computer Aided Metabolic Engineering and Optimization of Cell Factories

Computational systems biology methods enable rational design of cell factories on a genome-scale and thus accelerate the engineering of cells for the production of valuable chemicals and proteins. Unfortunately, the majority of these methods' implementations are either not published, rely on proprietary software, or do not provide documented interfaces, which has precluded their mainstream adoption in the field. In this work we present cameo, a platform-independent software that enables in silico design of cell factories and targets both experienced modelers as well as users new to the field. It is written in Python and implements state-of-the-art methods for enumerating and prioritizing knockout, knock-in, overexpression, and down-regulation strategies and combinations thereof. Cameo is an open source software project and is freely available under the Apache License 2.0. A dedicated Web site including documentation, examples, and installation instructions can be found at http://cameo.bio . Users can also give cameo a try at http://try.cameo.bio .

Pathway Design, Engineering, and Optimization

The microbial metabolic versatility found in nature has inspired scientists to create microorganisms capable of producing value-added compounds. Many endeavors have been made to transfer and/or combine pathways, existing or even engineered enzymes with new function to tractable microorganisms to generate new metabolic routes for drug, biofuel, and specialty chemical production. However, the success of these pathways can be impeded by different complications from an inherent failure of the pathway to cell perturbations. Pursuing ways to overcome these shortcomings, a wide variety of strategies have been developed. This chapter will review the computational algorithms and experimental tools used to design efficient metabolic routes, and construct and optimize biochemical pathways to produce chemicals of high interest.

Information system on microbial collections as a part of bioresource collections portal for Russia’s FASO organizations: a working protocol

Nowadays, many scientific organizations of Russia own collections of microorganisms on which large volumes of information have been generated. These data represent the descriptions of objects of diverse nature (bacteria, archaea, fungi, protists) and their properties, which have been carefully collected and cataloged by generations of researchers. Not every organization that has such collections has an open access electronic catalog, which not only complicates work with these unique materials, but also even hides the fact of the existence of such collections. This state of affairs requires the development of electronic resources for presenting these materials to the scientific community. To put together the information on microorganism collections, we have developed an internet portal (http://www.biores.cytogen.ru/microbes/) of microbial bioresource collections of FASO organizations in the Russian Federation. The portal was created under the project developing the information system for bioresource collections of FASO institutes. It is a platform where collection organizations can place information about the storage units of their collections, as well as other information on collections, including links to their own catalogs. In this paper, we describe the principles of working with the portal. The portal’s graphical interface allows users, both registered and unregistered, to receive the following information about collections of microorganisms: a list of collections represented in the database, contact details of the organization and information about the curator of the collection, summary statistics for each collection, as well as information on storage units. Registered users – owners of collections – have the opportunity to create and modify records about the storage units of their collections, and to update their description. To automate work with the portal, software access to the database through the REST API has been implemented (http://api.biores.cytogen.ru/ microbes/). At present, the portal is still being filled, but it already contains a description of more than 13,000 items of storage (of which 3500 are in the microorganisms’ part) of 65 bioresource collections in Russia’s FASO organizations. Of these collections, 12 with microorganisms have a total diversity of funds of about 50,000 strains).

Redox cofactor engineering in industrial microorganisms: strategies, recent applications and future directions

NAD and NADP, a pivotal class of cofactors, which function as essential electron donors or acceptors in all biological organisms, drive considerable catabolic and anabolic reactions. Furthermore, they play critical roles in maintaining intracellular redox homeostasis. However, many metabolic engineering efforts in industrial microorganisms towards modification or introduction of metabolic pathways, especially those involving consumption, generation or transformation of NAD/NADP, often induce fluctuations in redox state, which dramatically impede cellular metabolism, resulting in decreased growth performance and biosynthetic capacity. Here, we comprehensively review the cofactor engineering strategies for solving the problematic redox imbalance in metabolism modification, as well as their features, suitabilities and recent applications. Some representative examples of in vitro biocatalysis are also described. In addition, we briefly discuss how tools and methods from the field of synthetic biology can be applied for cofactor engineering. Finally, future directions and challenges for development of cofactor redox engineering are presented.

The power of synthetic biology for bioproduction, remediation and pollution control: The UN's Sustainable Development Goals will inevitably require the application of molecular biology and biotechnology on a global scale

The UN's Sustainable Development Goals present a challenge for biotechnology to develop new environmentally‐friendly and sustainable products and production processes.

Protein engineering of α-ketoisovalerate decarboxylase for improved isobutanol production in Synechocystis PCC 6803

Protein engineering is a powerful tool to modify e.g. protein stability, activity and substrate selectivity. Heterologous expression of the enzyme α-ketoisovalerate decarboxylase (Kivd) in the unicellular cyanobacterium Synechocystis PCC 6803 results in cells producing isobutanol and 3-methyl-1-butanol, with Kivd identified as a potential bottleneck. In the present study, we used protein engineering of Kivd to improve isobutanol production in Synechocystis PCC 6803. Isobutanol is a flammable compound that can be used as a biofuel due to its high energy density and suitable physical and chemical properties. Single replacement, either Val461 to isoleucine or Ser286 to threonine, increased the Kivd activity significantly, both in vivo and in vitro resulting in increased overall production while isobutanol production was increased more than 3-methyl-1-butanol production. Moreover, among all the engineered strains examined, the strain with the combined modification V461I/S286T showed the highest (2.4 times) improvement of isobutanol-to-3M1B molar ratio, which was due to a decrease of the activity towards 3M1B production. Protein engineering of Kivd resulted in both enhanced total catalytic activity and preferential shift towards isobutanol production in Synechocystis PCC 6803.

Genome-scale metabolic models as platforms for identification of novel genes as antimicrobial drug targets

The growing number of multidrug-resistant pathogenic bacteria is becoming a world leading challenge for the scientific community and for public health. However, advances in high-throughput technologies and whole-genome sequencing of bacterial pathogens make the construction of bacterial genome-scale metabolic models (GEMs) increasingly realistic. The use of GEMs as an alternative platforms will expedite identification of novel unconditionally essential genes and enzymes of target organisms with existing and forthcoming GEMs. This approach will follow the existing protocol for construction of high-quality GEMs, which could ultimately reduce the time, cost and labor-intensive processes involved in identification of novel antimicrobial drug targets in drug discovery pipelines. We discuss the current impact of existing GEMs of selected multidrug-resistant pathogenic bacteria for identification of novel antimicrobial drug targets and the challenges of closing the gap between genome-scale metabolic modeling and conventional experimental trial-and-error approaches in drug discovery pipelines.

Diverse genetic error modes constrain large-scale bio-based production

A transition toward sustainable bio-based chemical production is important for green growth. However, productivity and yield frequently decrease as large-scale microbial fermentation progresses, commonly ascribed to phenotypic variation. Yet, given the high metabolic burden and toxicities, evolutionary processes may also constrain bio-based production. We experimentally simulate large-scale fermentation with mevalonic acid-producing Escherichia coli. By tracking growth rate and production, we uncover how populations fully sacrifice production to gain fitness within 70 generations. Using ultra-deep (>1000×) time-lapse sequencing of the pathway populations, we identify multiple recurring intra-pathway genetic error modes. This genetic heterogeneity is only detected using deep-sequencing and new population-level bioinformatics, suggesting that the problem is underestimated. A quantitative model explains the population dynamics based on enrichment of spontaneous mutant cells. We validate our model by tuning production load and escape rate of the production host and apply multiple orthogonal strategies for postponing genetically driven production declines.

The declining performance of scale-up bioreactor cultures is commonly attributed to phenotypic and physical heterogeneities. Here, the authors reveal multiple recurring intra-pathway error modes that limit engineered E. coli mevalonic acid production over time- and industrial-scale fermentations.

Synthetic addiction extends the productive life time of engineered Escherichia coli populations

Bioproduction of chemicals offers a sustainable alternative to petrochemical synthesis routes by using genetically engineered microorganisms to convert waste and simple substrates into higher-value products. However, efficient high-yield production commonly introduces a metabolic burden that selects for subpopulations of nonproducing cells in large fermentations. To postpone such detrimental evolution, we have synthetically addicted production cells to production by carefully linking signals of product presence to expression of nonconditionally essential genes. We addict Escherichia coli cells to their engineered biosynthesis of mevalonic acid by fine-tuned control of essential genes using a product-responsive transcription factor. Over the course of a long-term fermentation equivalent to industrial 200-m³ bioreactors such addicted cells remained productive, unlike the control, in which evolution fully terminated production.

Bio-production of chemicals is an important driver of the societal transition toward sustainability. However, fermentations with heavily engineered production organisms can be challenging to scale to industrial volumes. Such fermentations are subject to evolutionary pressures that select for a wide range of genetic variants that disrupt the biosynthetic capacity of the engineered organism. Synthetic product addiction that couples high-yield production of a desired metabolite to expression of nonconditionally essential genes could offer a solution to this problem by selectively favoring cells with biosynthetic capacity in the population without constraining the medium. We constructed such synthetic product addiction by controlling the expression of two nonconditionally essential genes with a mevalonic acid biosensor. The product-addicted production organism retained high-yield mevalonic acid production through 95 generations of cultivation, corresponding to the number of cell generations required for >200-m³ industrial-scale production, at which time the nonaddicted strain completely abolished production. Using deep DNA sequencing, we find that the product-addicted populations do not accumulate genetic variants that compromise biosynthetic capacity, highlighting how synthetic networks can be designed to control genetic population heterogeneity. Such synthetic redesign of evolutionary forces with endogenous processes may be a promising concept for realizing complex cellular designs required for sustainable bio-manufacturing.

Exploiting transcriptomic data for metabolic engineering: toward a systematic strain design

Transcriptomics is now recognized as a primary tool for metabolic engineering as it can be used for identifying new strain designs by diagnosing current states of microbial cells. This review summarizes current application of transcriptomic data for strain design. Along with a few successful examples, limitations of conventionally used differentially expressed gene-based strain design approaches have been discussed, which have been major reasons why transcriptomic data are considerably underutilized. Recently, integrative network-based approaches interpreting transcriptomic data in the context of biological networks were invented to provide complimentary solutions for metabolic engineering by overcoming the limitations of conventional approaches. Here, we highlight recent pioneering studies in which integrative network-based methods have been used for providing novel strain designs.

Rewriting the Metabolic Blueprint: Advances in Pathway Diversification in Microorganisms

Living organisms have evolved over millions of years to fine tune their metabolism to create efficient pathways for producing metabolites necessary for their survival. Advancement in the field of synthetic biology has enabled the exploitation of these metabolic pathways for the production of desired compounds by creating microbial cell factories through metabolic engineering, thus providing sustainable routes to obtain value-added chemicals. Following the past success in metabolic engineering, there is increasing interest in diversifying natural metabolic pathways to construct non-natural biosynthesis routes, thereby creating possibilities for producing novel valuable compounds that are non-natural or without elucidated biosynthesis pathways. Thus, the range of chemicals that can be produced by biological systems can be expanded to meet the demands of industries for compounds such as plastic precursors and new antibiotics, most of which can only be obtained through chemical synthesis currently. Herein, we review and discuss novel strategies that have been developed to rewrite natural metabolic blueprints in a bid to broaden the chemical repertoire achievable in microorganisms. This review aims to provide insights on recent approaches taken to open new avenues for achieving biochemical production that are beyond currently available inventions.