Metabolic Engineering Applications Research Articles

Genome-Scale Metabolic Modeling of Escherichia coli and Its Chassis Design for Synthetic Biology Applications.

Genome-scale metabolic modeling is and will continue to play a central role in computational systems metabolic engineering and synthetic biology applications for the productions of chemicals and antibiotics. To that end, a survey and workflows of methods used for the development of high-quality genome-scale metabolic models (GEMs) and chassis design for synthetic biology are described here. The chapter consists of two parts (a) the methods of development of GEMs (Escherichia coli as a case study) and (b) E. coli chassis design for synthetic production of 1,4-butanediol (BDO). The methods described here can guide existing and future development of GEMs coupled with host chassis design for synthetic productions of novel antibiotics.

Gene amplification, laboratory evolution, and biosensor screening reveal MucK as a terephthalic acid transporter in Acinetobacter baylyi ADP1

Microbial terephthalic acid (TPA) catabolic pathways are conserved among the few bacteria known to turnover this xenobiotic aromatic compound. However, to date there are few reported cases in which this pathway has been successfully expressed in heterologous hosts to impart efficient utilization of TPA as a sole carbon source. In this work, we aimed to engineer TPA conversion in Acinetobacter baylyi ADP1 via the heterologous expression of catabolic and transporter genes from a native TPA-utilizing bacterium. Specifically, we obtained ADP1-derived strains capable of growing on TPA as the sole carbon source using chromosomal insertion and targeted amplification of the tph catabolic operon from Comamonas sp. E6. Adaptive laboratory evolution was then used to improve growth on this substrate. TPA consumption rates of the evolved strains, which retained multiple copies of the tph genes, were ~0.2 g/L/h (or ~1 g TPA/g cells/h), similar to that of Comamonas sp. E6 and almost 2-fold higher than that of Rhodococcus jostii RHA1, another native TPA-utilizing strain. To evaluate TPA transport in the evolved ADP1 strains, we engineered a TPA biosensor consisting of the transcription factor TphR and a fluorescent reporter. In combination with whole-genome sequencing, the TPA biosensor revealed that transport of TPA was not mediated by the heterologous proteins from Comamonas sp. E6. Instead, the endogenous ADP1 muconate transporter MucK, a member of the major facilitator superfamily, was responsible for TPA transport in several evolved strains in which MucK variants were found to enhance TPA uptake. Furthermore, the IclR-type transcriptional regulator DcaS was identified as a repressor of mucK expression. Overall, this work presents an unexpected function of a native protein identified through gene amplification, adaptive laboratory evolution, and a combination of screening methods. This study also provides a TPA biosensor for application in ADP1 and identifies transporter variants for use in metabolic engineering applications focused on plastic upcycling of polyesters.

ERF Gene Clusters: Working Together to Regulate Metabolism.

Plants produce structurally diverse specialized metabolites, including bioactive alkaloids and terpenoids, in response to biotic and abiotic environmental stresses. The APETALA2/ETHYLENE RESPONSE FACTOR (AP2/ERF) family of transcription factors (TFs) play key roles in regulating biosynthesis of specialized metabolites. Increasing genomic and functional evidence shows that a subset of the ERF genes occurs in clusters on the chromosomes. These jasmonate-responsive ERF TF gene clusters control the biosynthesis of many important metabolites, from natural products, such as nicotine and steroidal glycoalkaloids (SGAs), to pharmaceuticals, such as artemisinin, vinblastine, and vincristine. Here, we review the function, regulation, and evolution of ERF clusters and highlight recent advances in understanding the distinct roles of clustered ERF genes and their possible application in metabolic engineering.

A Single Cas9-VPR Nuclease for Simultaneous Gene Activation, Repression, and Editing in Saccharomyces cerevisiae.

Combinatorial metabolic engineering has been widely established for the development of efficient microbial cell factories to produce the products of interest by precisely regulating the expression levels of multiple genes simultaneously. Here, we report a novel multifunctional CRISPR system that enables simultaneous gene activation, repression, and editing (CRISPR-ARE) with a single Cas9-VPR protein for combinatorial metabolic engineering applications in Saccharomyces cerevisiae. Via gRNA engineering, we achieved orthogonal transcriptional regulations and genome editing using the nuclease active Cas9-VPR fusion protein, individually or in a combinatorial manner. After establishing a system for stable expression of multiple gRNAs on the same plasmid, we first demonstrated CRISPR-ARE for simultaneous mCherry activation, mVenus repression, and ADE2 disruption in a fluorescence reporter strain. Subsequently, we adopted CRISPR-ARE for simple and fast combinatorial metabolic engineering, which improved the production of α-santalene for 2.66-fold in a single step. Because of its simplicity and modularity, the developed CRISPR-ARE system could be applied for facile multifunctional metabolic engineering of microbial cell factories, particularly for which only a few CRISPR proteins have been characterized.

The 3-ketoacyl-CoA thiolase: an engineered enzyme for carbon chain elongation of chemical compounds.

Because of their function of catalyzing the rearrangement of the carbon chains, thiolases have attracted increasing attentions over the past decades. The 3-ketoacyl-CoA thiolase (KAT) is a member of the thiolase, which is capable of catalyzing the Claisen condensation reaction between the two acyl-CoAs, thereby achieving carbon chain elongation. In this way, diverse value-added compounds might be synthesized starting from simple small CoA thioesters. However, most KATs are hampered by low stability and poor substrate specificity, which has hindered the development of large-scale biosynthesis. In this review, the common characteristics in the three-dimensional structure of KATs from different sources are summarized. Moreover, structure-guided rational engineering is discussed as a strategy for enhancing the performance of KATs. Finally, we reviewed the metabolic engineering applications of KATs for producing various energy-storage molecules, such as n-butanol, fatty acids, dicarboxylic acids, and polyhydroxyalkanoates. KEY POINTS: • Summarize the structural characteristics and catalyzation mechanisms of KATs. • Review on the rational engineering to enhance the performance of KATs. • Discuss the applications of KATs for producing energy-storage molecules.

Promoter Architecture and Promoter Engineering in Saccharomyces cerevisiae.

Promoters play an essential role in the regulation of gene expression for fine-tuning genetic circuits and metabolic pathways in Saccharomyces cerevisiae (S. cerevisiae). However, native promoters in S. cerevisiae have several limitations which hinder their applications in metabolic engineering. These limitations include an inadequate number of well-characterized promoters, poor dynamic range, and insufficient orthogonality to endogenous regulations. Therefore, it is necessary to perform promoter engineering to create synthetic promoters with better properties. Here, we review recent advances related to promoter architecture, promoter engineering and synthetic promoter applications in S. cerevisiae. We also provide a perspective of future directions in this field with an emphasis on the recent advances of machine learning based promoter designs.

CRISPR-Assisted Multiplex Base Editing System in Pseudomonas putida KT2440

Pseudomonas putida (P. putida) KT2440 is a paradigmatic environmental-bacterium that possesses significant potential in synthetic biology, metabolic engineering and biodegradation applications. However, most genome editing methods of P. putida KT2440 depend on heterologous repair proteins and the provision of donor DNA templates, which is laborious and inefficient. In this report, an efficient cytosine base editing system was established by using cytidine deaminase (APOBEC1), enhanced specificity Cas9 nickase (eSpCas9ppD10A) and the uracil DNA glycosylase inhibitor (UGI). This constructed base editor converts C-G into T-A in the absence of DNA strands breaks and donor DNA templates. By introducing a premature stop codon in target spacers, we successfully applied this system for gene inactivation with an efficiency of 25–100% in various Pseudomonas species, including P. putida KT2440, P. aeruginosa PAO1, P. fluorescens Pf-5 and P. entomophila L48. We engineered an eSpCas9ppD10A-NG variant with a NG protospacer adjacent motif to expand base editing candidate sites. By modifying the APOBEC1 domain, we successfully narrowed the editable window to increase gene inactivation efficiency in cytidine-rich spacers. Additionally, multiplex base editing in double and triple loci was achieved with mutation efficiencies of 90–100% and 25–35%, respectively. Taken together, the establishment of a fast, convenient and universal base editing system will accelerate the pace of future research undertaken with P. putida KT2440 and other Pseudomonas species.

An extended and generalized framework for the calculation of metabolic intervention strategies based on minimal cut sets.

The concept of minimal cut sets (MCS) provides a flexible framework for analyzing properties of metabolic networks and for computing metabolic intervention strategies. In particular, it has been used to support the targeted design of microbial strains for bio-based production processes. Herein we present a number of major extensions that generalize the existing MCS approach and broaden its scope for applications in metabolic engineering. We first introduce a modified approach to integrate gene-protein-reaction associations (GPR) in the metabolic network structure for the computation of gene-based intervention strategies. In particular, we present a set of novel compression rules for GPR associations, which effectively speedup the computation of gene-based MCS by a factor of up to one order of magnitude. These rules are not specific for MCS and as well applicable to other computational strain design methods. Second, we enhance the MCS framework by allowing the definition of multiple target (undesired) and multiple protected (desired) regions. This enables precise tailoring of the metabolic solution space of the designed strain with unlimited flexibility. Together with further generalizations such as individual cost factors for each intervention, direct combinations of reaction/gene deletions and additions as well as the possibility to search for substrate co-feeding strategies, the scope of the MCS framework could be broadly extended. We demonstrate the applicability and performance benefits of the described developments by computing (gene-based) Escherichia coli strain designs for the bio-based production of 2,3-butanediol, a chemical, that has recently received much attention in the field of metabolic engineering. With our extended framework, we could identify promising strain designs that were formerly unpredictable, including those based on substrate co-feeding.

Multiplexing cell-cell communication.

The engineering of advanced multicellular behaviors, such as the programmed growth of biofilms or tissues, requires cells to communicate multiple aspects of physiological information. Unfortunately, few cell‐cell communication systems have been developed for synthetic biology. Here, we engineer a genetically encoded channel selector device that enables a single communication system to transmit two separate intercellular conversations. Our design comprises multiplexer and demultiplexer sub‐circuits constructed from a total of 12 CRISPRi‐based transcriptional logic gates, an acyl homoserine lactone‐based communication module, and three inducible promoters that enable small molecule control over the conversations. Experimentally parameterized mathematical models of the sub‐components predict the steady state and dynamical performance of the full system. Multiplexed cell‐cell communication has applications in synthetic development, metabolic engineering, and other areas requiring the coordination of multiple pathways among a community of cells.

The Evolution of Cell Free Biomanufacturing

Cell-free systems are a widely used research tool in systems and synthetic biology and a promising platform for manufacturing of proteins and chemicals. In the past, cell-free biology was primarily used to better understand fundamental biochemical processes. Notably, E. coli cell-free extracts were used in the 1960s to decipher the sequencing of the genetic code. Since then, the transcription and translation capabilities of cell-free systems have been repeatedly optimized to improve energy efficiency and product yield. Today, cell-free systems, in combination with the rise of synthetic biology, have taken on a new role as a promising technology for just-in-time manufacturing of therapeutically important biologics and high-value small molecules. They have also been implemented at an industrial scale for the production of antibodies and cytokines. In this review, we discuss the evolution of cell-free technologies, in particular advancements in extract preparation, cell-free protein synthesis, and cell-free metabolic engineering applications. We then conclude with a discussion of the mathematical modeling of cell-free systems. Mathematical modeling of cell-free processes could be critical to addressing performance bottlenecks and estimating the costs of cell-free manufactured products.

Current understanding of the cyanobacterial CRISPR-Cas systems and development of the synthetic CRISPR-Cas systems for cyanobacteria

Cyanobacteria are photosynthetic microorganisms that are capable of converting CO2 to value-added chemicals. Engineering of cyanobacteria with synthetic biology tools, including the CRISPR-Cas system, has allowed an opportunity for biological CO2 utilization. Here, we described natural CRISPR-Cas systems for understanding cyanobacterial genomics and synthetic CRISPR-Cas systems for metabolic engineering applications. The natural CRISPR-Cas systems in cyanobacteria have been identified as Class 1, with type I and III, and some Class 2, with type V, as an adaptive immune system against viral invasion. As synthetic tools, CRISPR-Cas9 and -Cas12a have been successfully established in cyanobacteria to delete a target gene without a selection marker. Deactivated Cas9 and Cas12a have also been used to repress genes for metabolic engineering. In addition, a perspective on how advanced CRISPR-Cas systems and a pool of the guide RNAs can be advantageous for precise genome engineering and understanding of unknown functions was discussed for advanced engineering of cyanobacteria.

Key Challenges in Designing CHO Chassis Platforms

Following the success of and the high demand for recombinant protein-based therapeutics during the last 25 years, the pharmaceutical industry has invested significantly in the development of novel treatments based on biologics. Mammalian cells are the major production systems for these complex biopharmaceuticals, with Chinese hamster ovary (CHO) cell lines as the most important players. Over the years, various engineering strategies and modeling approaches have been used to improve microbial production platforms, such as bacteria and yeasts, as well as to create pre-optimized chassis host strains. However, the complexity of mammalian cells curtailed the optimization of these host cells by metabolic engineering. Most of the improvements of titer and productivity were achieved by media optimization and large-scale screening of producer clones. The advances made in recent years now open the door to again consider the potential application of systems biology approaches and metabolic engineering also to CHO. The availability of a reference genome sequence, genome-scale metabolic models and the growing number of various “omics” datasets can help overcome the complexity of CHO cells and support design strategies to boost their production performance. Modular design approaches applied to engineer industrially relevant cell lines have evolved to reduce the time and effort needed for the generation of new producer cells and to allow the achievement of desired product titers and quality. Nevertheless, important steps to enable the design of a chassis platform similar to those in use in the microbial world are still missing. In this review, we highlight the importance of mammalian cellular platforms for the production of biopharmaceuticals and compare them to microbial platforms, with an emphasis on describing novel approaches and discussing still open questions that need to be resolved to reach the objective of designing enhanced modular chassis CHO cell lines.

Identification of α-Amyrin 28-Carboxylase and Glycosyltransferase From Ilex asprella and Production of Ursolic Acid 28-O-β-D-Glucopyranoside in Engineered Yeast.

Ilex asprella is a medicinal plant that is used extensively in southern China. The plant contains ursane-type triterpenoids and triterpenoid saponins which are known to be responsible for its pharmacological activities. Previously, a transcriptomic analysis of I. asprella was carried out and the gene IaAS1, which is important in the formation of the core structure α-amyrin, was identified. However, the genes related to the subsequent derivatization of the core structures of the triterpenoid remain largely unknown. Herein, we describe the cloning and functional characterization of an amyrin 28-carboxylase IaAO1 (designated as IaCYP716A210) and a glycosyltransferase IaAU1 (designated as UGT74AG5), based on transcriptomic data. The expression of IaAO1 in an α-amyrin producing yeast strain led to the accumulation of ursolic acid. An enzyme assay using recombinant protein IaAU1 purified from E. coli revealed that IaAU1 can catalyze the conversion of ursolic acid to ursolic acid 28-O-β-D-glucopyranoside. IaAU1 has regiospecificity for catalyzing the 28-O-glucosylation of ursane-/oleanane-type triterpene acids, as it can also catalyze the conversion of oleanolic acid, hederagenin, and ilexgenin A to their corresponding glycosyl compounds. Moreover, co-expression of IaAO1 and IaAU1 in the α-amyrin-producing yeast strain led to the production of ursolic acid 28-O-β-D-glucopyranoside, although in relatively low amounts. Our study reveals that IaAO1 and IaAU1 might play a role in the biosynthesis of pentacyclic triterpenoid saponins in I. asprella and provides insights into the potential application of metabolic engineering to produce ursane-type triterpene glycosides.

Synthetic promoter design in Escherichia coli based on a deep generative network.

Promoter design remains one of the most important considerations in metabolic engineering and synthetic biology applications. Theoretically, there are 450 possible sequences for a 50-nt promoter, of which naturally occurring promoters make up only a small subset. To explore the vast number of potential sequences, we report a novel AI-based framework for de novo promoter design in Escherichia coli. The model, which was guided by sequence features learned from natural promoters, could capture interactions between nucleotides at different positions and design novel synthetic promoters in silico. We combined a deep generative model that guides the search for artificial sequences with a predictive model to preselect the most promising promoters. The AI-designed promoters were optimized based on the promoter activity in E. coli and the predictive model. After two rounds of optimization, up to 70.8% of the AI-designed promoters were experimentally demonstrated to be functional, and few of them shared significant sequence similarity with the E. coli genome. Our work provided an end-to-end approach to the de novo design of novel promoter elements, indicating the potential to apply deep learning methods to de novo genetic element design.

Dynamic Flux Balance Analysis to Evaluate the Strain Production Performance on Shikimic Acid Production in Escherichia coli.

Flux balance analysis (FBA) is used to improve the microbial production of useful compounds. However, a large gap often exists between the FBA solution and the experimental yield, because of growth and byproducts. FBA has been extended to dynamic FBA (dFBA), which is applicable to time-varying processes, such as batch or fed-batch cultures, and has significantly contributed to metabolic and cultural engineering applications. On the other hand, the performance of the experimental strains has not been fully evaluated. In this study, we applied dFBA to the production of shikimic acid from glucose in Escherichia coli, to evaluate the production performance of the strain as a case study. The experimental data of glucose consumption and cell growth were used as FBA constraints. Bi-level FBA optimization with maximized growth and shikimic acid production were the objective functions. Results suggest that the shikimic acid concentration in the high-shikimic-acid-producing strain constructed in the experiment reached up to 84% of the maximum value by simulation. Thus, this method can be used to evaluate the performance of strains and estimate the milestones of strain improvement.

A reconciliation of genome-scale metabolic network model of Zymomonas mobilis ZM4

Zymomonas mobilis ZM4 has recently been used for a variety of biotechnological purposes. To rationally enhance its metabolic performance, a reliable genome-scale metabolic network model (GEM) of this organism is required. To this end, we reconstructed a genome-scale metabolic model (iHN446) for Z. mobilis, which involves 446 genes, 859 reactions, and 894 metabolites. We started by first reconciling the existing GEMs previously constructed for Z. mobilis to obtain a draft network. Next, recent gene annotations, up-to-date literature, physiological data and biochemical databases were used to upgrade the network. Afterward, the draft network went through a curative and iterative process of gap-filling by computational tools and manual refinement. The final model was evaluated using experimental data and literature information. We next applied this model as a platform for analyzing the links between transcriptome-flux and transcriptome-metabolome. We found that experimental observations were in agreement with the predicted results from our final GEM. Taken together, this comprehensive model (iHN446) can be utilized for studying metabolism in Z. mobilis and finding rational targets for metabolic engineering applications.

Translation Related Factors Improve the Productivity of a Streptomyces-Based Cell-Free Protein Synthesis System.

Cell-free protein synthesis (CFPS) systems are emerging as effective platforms for the production of recombinant proteins in vitro. To enable the expression of various proteins, different CFPS systems have been developed to better mimic the cellular environment of native hosts. In this context, a Streptomyces-based CFPS system was recently developed to express high GC-content genes. Unfortunately, protein yields from the initial system were relatively low (∼50 μg/mL). Here, we sought to address this limitation and enhance the productivity of the Streptomyces-based CFPS system. By adding protein translation related factors to CFPS reactions, we were able to achieve protein yields of approximately 400 μg/mL, which is the highest yield reported to date. We expect our enhanced Streptomyces CFPS system will set the stage for novel applications in metabolic engineering and synthetic biology such as in vitro discovery and synthesis of natural products, which are produced by Streptomyces species with high GC-content (>70%) genomes.

Flux-based hierarchical organization of Escherichia coli's metabolic network.

Biological networks across scales exhibit hierarchical organization that may constrain network function. Yet, understanding how these hierarchies arise due to the operational constraint of the networks and whether they impose limits to molecular phenotypes remains elusive. Here we show that metabolic networks include a hierarchy of reactions based on a natural flux ordering that holds for every steady state. We find that the hierarchy of reactions is reflected in experimental measurements of transcript, protein and flux levels of Escherichia coli under various growth conditions as well as in the catalytic rate constants of the corresponding enzymes. Our findings point at resource partitioning and a fine-tuning of enzyme levels in E. coli to respect the constraints imposed by the network structure at steady state. Since reactions in upper layers of the hierarchy impose an upper bound on the flux of the reactions downstream, the hierarchical organization of metabolism due to the flux ordering has direct applications in metabolic engineering.

Dual graph convolutional neural network for predicting chemical networks

BackgroundPredicting of chemical compounds is one of the fundamental tasks in bioinformatics and chemoinformatics, because it contributes to various applications in metabolic engineering and drug discovery. The recent rapid growth of the amount of available data has enabled applications of computational approaches such as statistical modeling and machine learning method. Both a set of chemical interactions and chemical compound structures are represented as graphs, and various graph-based approaches including graph convolutional neural networks have been successfully applied to chemical network prediction. However, there was no efficient method that can consider the two different types of graphs in an end-to-end manner.ResultsWe give a new formulation of the chemical network prediction problem as a link prediction problem in a graph of graphs (GoG) which can represent the hierarchical structure consisting of compound graphs and an inter-compound graph. We propose a new graph convolutional neural network architecture called dual graph convolutional network that learns compound representations from both the compound graphs and the inter-compound network in an end-to-end manner.ConclusionsExperiments using four chemical networks with different sparsity levels and degree distributions shows that our dual graph convolution approach achieves high prediction performance in relatively dense networks, while the performance becomes inferior on extremely-sparse networks.

Characterizing glucose, illumination, and nitrogen-deprivation phenotypes of Synechocystis PCC6803 with Raman spectroscopy.

BackgroundSynechocystis sp. PCC6803 is a model cyanobacterium that has been studied widely and is considered for metabolic engineering applications. Here, Raman spectroscopy and Raman chemometrics (Rametrix™) were used to (i) study broad phenotypic changes in response to growth conditions, (ii) identify phenotypic changes associated with its circadian rhythm, and (iii) correlate individual Raman bands with biomolecules and verify these with more accepted analytical methods.MethodsSynechocystis cultures were grown under various conditions, exploring dependencies on light and/or external carbon and nitrogen sources. The Rametrix™ LITE Toolbox for MATLAB® was used to process Raman spectra and perform principal component analysis (PCA) and discriminant analysis of principal components (DAPC). The Rametrix™ PRO Toolbox was used to validate these models through leave-one-out routines that classified a Raman spectrum when growth conditions were withheld from the model. Performance was measured by classification accuracy, sensitivity, and specificity. Raman spectra were also subjected to statistical tests (ANOVA and pairwise comparisons) to identify statistically relevant changes in Synechocystis phenotypes. Finally, experimental methods, including widely used analytical and spectroscopic assays were used to quantify the levels of glycogen, fatty acids, amino acids, and chlorophyll a for correlations with Raman data.ResultsPCA and DAPC models produced distinct clustering of Raman spectra, representing multiple Synechocystis phenotypes, based on (i) growth in the presence of 5 mM glucose, (ii) illumination (dark, light/dark [12 h/12 h], and continuous light at 20 µE), (iii) nitrogen deprivation (0–100% NaNO3 of native BG-11 medium in continuous light), and (iv) throughout a 24 h light/dark (12 h/12 h) circadian rhythm growth cycle. Rametrix™ PRO was successful in identifying glucose-induced phenotypes with 95.3% accuracy, 93.4% sensitivity, and 96.9% specificity. Prediction accuracy was above random chance values for all other studies. Circadian rhythm analysis showed a return to the initial phenotype after 24 hours for cultures grown in light/dark (12 h/12 h) cycles; this did not occur for cultures grown in the dark. Finally, correlation coefficients (R > 0.7) were found for glycogen, all amino acids, and chlorophyll a when comparing specific Raman bands to other experimental results.