In silico genome-scale reconstruction and validation of the Corynebacterium glutamicum metabolic network

Promising non-model microbial cell factories obtained by genome reduction

The development of sustainable processes is the most important basis to realize the shift from the fossil-fuel based industry to bio-based production. Non-model microbes represent a great resource due to their advantageous traits and unique repertoire of bioproducts. However, most of these microbes require modifications to improve their growth and production capacities as well as robustness in terms of genetic stability. For this, genome reduction is a valuable and powerful approach to meet industry requirements and to design highly efficient production strains. Here, we provide an overview of various genome reduction approaches in prokaryotic microorganisms, with a focus on non-model organisms, and highlight the example of a successful genome-reduced model organism chassis. Furthermore, we discuss the advances and challenges of promising non-model microbial chassis.

Production of L-serine and its derivative L-cysteine from renewable feedstocks using Corynebacterium glutamicum: advances and perspectives

L-serine and its derivative L-cysteine have broad industrial applications, and their direct fermentative production from renewable biomass is gaining increasing attention. Corynebacterium glutamicum is an extensively studied and well-established industrial microorganism, which is a predominant microbial host for producing amino acids. In this review, updated information on the genetics and molecular mechanisms underlying L-serine and L-cysteine production using C. glutamicum is presented, including their synthesis and degradation pathways, and other intracellular processes related to their production, as well as the mechanisms underlying substrate import and product export are also analyzed. Furthermore, metabolic strategies for strain improvement are systematically discussed, and conclusions and future perspectives for bio-based L-serine and L-cysteine production using C. glutamicum are presented. This review can provide a thorough understanding of L-serine and L-cysteine metabolic pathways to facilitate metabolic engineering modifications of C. glutamicum and development of more efficient industrial fermentation processes for L-serine and L-cysteine production.

Genome-scale metabolic network models for industrial microorganisms metabolic engineering: Current advances and future prospects

The construction of high-performance microbial cell factories (MCFs) is the centerpiece of biomanufacturing. However, the complex metabolic regulatory network of microorganisms poses great challenges for the efficient design and construction of MCFs. The genome-scale metabolic network models (GSMs) can systematically simulate the metabolic regulation process of microorganisms in silico, providing effective guidance for the rapid design and construction of MCFs. In this review, we summarized the development status of 16 important industrial microbial GSMs, and further outline the technologies or methods that continuously promote high-quality GSMs construction from five aspects: I) Databases and modeling tools facilitate GSMs reconstruction; II) evolving gap-filling technologies; III) constraint-based model reconstruction; IV) advances in algorithms; and V) developed visualization tools. In addition, we also summarized the applications of GSMs in guiding metabolic engineering from four aspects: I) exploring and explaining metabolic features; II) predicting the effects of genetic perturbations on metabolism; III) predicting the optimal phenotype; IV) guiding cell factories construction in practical experiment. Finally, we discussed the development of GSMs, aiming to provide a reference for efficiently reconstructing GSMs and guiding metabolic engineering.

ThermusQ: Toward the cell simulation platform for Thermus thermophilus

ThermusQ is a website (https://www.thermusq.net/) that aims to gather all the molecular information on Thermus thermophilus and to provide a platform to easily access the whole view of the bacterium. ThermusQ comprises the genome sequences of 22 strains from T. thermophilus and T. oshimai strains, plus the sequences of known Thermus phages. ThermusQ also contains information and map diagrams of pathways unique to Thermus strains. The website provides tools to retrieve sequence data in different ways. By gathering the whole data of T. thermophilus strains, the strainspecific characteristics was found. This bird's-eye view of the whole data will lead the research community to identify missing important data and the integration will provide a platform to conduct future biochemical simulations of the bacterium.

A manually curated compendium of expression profiles for the microbial cell factory Corynebacterium glutamicum

Corynebacterium glutamicum is the major host for the industrial production of amino acids and has become one of the best studied model organisms in microbial biotechnology. Rational strain construction has led to an improvement of producer strains and to a variety of novel producer strains with a broad substrate and product spectrum. A key factor for the success of these approaches is detailed knowledge of transcriptional regulation in C. glutamicum. Here, we present a large compendium of 927 manually curated microarray-based transcriptional profiles for wild-type and engineered strains detecting genome-wide expression changes of the 3,047 annotated genes in response to various environmental conditions or in response to genetic modifications. The replicates within the 927 experiments were combined to 304 microarray sets ordered into six categories that were used for differential gene expression analysis. Hierarchical clustering confirmed that no outliers were present in the sets. The compendium provides a valuable resource for future fundamental and applied research with C. glutamicum and contributes to a systemic understanding of this microbial cell factory.

Measurement(s)	Gene Expression Analysis
Technology Type(s)	Two Color Microarray
Factor Type(s)	WT condition A vs. WT condition B • Plasmid-based gene overexpression in parental strain vs. parental strain with empty vector control • Deletion mutant vs. parental strain
Sample Characteristic - Organism	Corynebacterium glutamicum
Sample Characteristic - Environment	laboratory environment
Sample Characteristic - Location	Germany

Hot isopropanol quenching procedure for automated microtiter plate scale 13C-labeling experiments

Background

Currently, the generation of genetic diversity for microbial cell factories outpaces the screening of strain variants with omics-based phenotyping methods. Especially isotopic labeling experiments, which constitute techniques aimed at elucidating cellular phenotypes and supporting rational strain design by growing microorganisms on substrates enriched with heavy isotopes, suffer from comparably low throughput and the high cost of labeled substrates.

Results

We present a miniaturized, parallelized, and automated approach to ¹³C-isotopic labeling experiments by establishing and validating a hot isopropanol quenching method on a robotic platform coupled with a microbioreactor cultivation system. This allows for the first time to conduct automated labeling experiments at a microtiter plate scale in up to 48 parallel batches. A further innovation enabled by the automated quenching method is the analysis of free amino acids instead of proteinogenic ones on said microliter scale. Capitalizing on the latter point and as a proof of concept, we present an isotopically instationary labeling experiment in Corynebacterium glutamicum ATCC 13032, generating dynamic labeling data of free amino acids in the process.

Conclusions

Our results show that a robotic liquid handler is sufficiently fast to generate informative isotopically transient labeling data. Furthermore, the amount of biomass obtained from a sub-milliliter cultivation in a microbioreactor is adequate for the detection of labeling patterns of free amino acids. Combining the innovations presented in this study, isotopically stationary and instationary automated labeling experiments can be conducted, thus fulfilling the prerequisites for ¹³C-metabolic flux analyses in high-throughput.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12934-022-01806-4.

Synthetic Biology Toolkits and Metabolic Engineering Applied in Corynebacterium glutamicum for Biomanufacturing

Corynebacterium glutamicum is an important workhorse in industrial white biotechnology. It has been widely applied in the producing processes of amino acids, fuels, and diverse value-added chemicals. With the continuous disclosure of genetic regulation mechanisms, various strategies and technologies of synthetic biology were used to design and construct C. glutamicum cells for biomanufacturing and bioremediation. This study mainly aimed to summarize the design and construction strategies of C. glutamicum-engineered strains, which were based on genomic modification, synthetic biological device-assisted metabolic flux optimization, and directed evolution-based engineering. Then, taking two important bioproducts (N-acetylglucosamine and hyaluronic acid) as examples, the applications of C. glutamicum cell factories were introduced. Finally, we discussed the current challenges and future development trends of C. glutamicum-engineered strain construction.

High-Quality Genome-Scale Reconstruction of Corynebacterium glutamicum ATCC 13032

Corynebacterium glutamicum belongs to the microbes of enormous biotechnological relevance. In particular, its strain ATCC 13032 is a widely used producer of L-amino acids at an industrial scale. Its apparent robustness also turns it into a favorable platform host for a wide range of further compounds, mainly because of emerging bio-based economies. A deep understanding of the biochemical processes in C. glutamicum is essential for a sustainable enhancement of the microbe's productivity. Computational systems biology has the potential to provide a valuable basis for driving metabolic engineering and biotechnological advances, such as increased yields of healthy producer strains based on genome-scale metabolic models (GEMs). Advanced reconstruction pipelines are now available that facilitate the reconstruction of GEMs and support their manual curation. This article presents iCGB21FR, an updated and unified GEM of C. glutamicum ATCC 13032 with high quality regarding comprehensiveness and data standards, built with the latest modeling techniques and advanced reconstruction pipelines. It comprises 1042 metabolites, 1539 reactions, and 805 genes with detailed annotations and database cross-references. The model validation took place using different media and resulted in realistic growth rate predictions under aerobic and anaerobic conditions. The new GEM produces all canonical amino acids, and its phenotypic predictions are consistent with laboratory data. The in silico model proved fruitful in adding knowledge to the metabolism of C. glutamicum: iCGB21FR still produces L-glutamate with the knock-out of the enzyme pyruvate carboxylase, despite the common belief to be relevant for the amino acid's production. We conclude that integrating high standards into the reconstruction of GEMs facilitates replicating validated knowledge, closing knowledge gaps, and making it a useful basis for metabolic engineering. The model is freely available from BioModels Database under identifier MODEL2102050001.

Insights on the Advancements of In Silico Metabolic Studies of Succinic Acid Producing Microorganisms: A Review with Emphasis on Actinobacillus succinogenes

Succinic acid (SA) is one of the top candidate value-added chemicals that can be produced from biomass via microbial fermentation. A considerable number of cell factories have been proposed in the past two decades as native as well as non-native SA producers. Actinobacillus succinogenes is among the best and earliest known natural SA producers. However, its industrial application has not yet been realized due to various underlying challenges. Previous studies revealed that the optimization of environmental conditions alone could not entirely resolve these critical problems. On the other hand, microbial in silico metabolic modeling approaches have lately been the center of attention and have been applied for the efficient production of valuable commodities including SA. Then again, literature survey results indicated the absence of up-to-date reviews assessing this issue, specifically concerning SA production. Hence, this review was designed to discuss accomplishments and future perspectives of in silico studies on the metabolic capabilities of SA producers. Herein, research progress on SA and A. succinogenes, pathways involved in SA production, metabolic models of SA-producing microorganisms, and status, limitations and prospects on in silico studies of A. succinogenes were elaborated. All in all, this review is believed to provide insights to understand the current scenario and to develop efficient mathematical models for designing robust SA-producing microbial strains.

An energetic profile of Corynebacterium glutamicum underpinned by measured biomass yield on ATP

The supply and usage of energetic cofactors in metabolism is a central concern for systems metabolic engineering, particularly in case of energy intensive products. One of the most important parameters for systems wide balancing of energetic cofactors is the ATP requirement for biomass formation Y_ATP/Biomass. Despite its fundamental importance, Y_ATP/Biomass values for non-fermentative organisms are still rough estimates deduced from theoretical considerations. For the first time, we present an approach for the experimental determination of Y_ATP/Biomass using comparative ¹³C metabolic flux analysis (¹³C MFA) of a wild type strain and an ATP synthase knockout mutant. We show that the energetic profile of a cell can then be deduced from a genome wide stoichiometric model and experimental maintenance data. Particularly, the contributions of substrate level phosphorylation (SLP) and electron transport phosphorylation (ETP) to ATP generation become available which enables the overall energetic efficiency of a cell to be characterized. As a model organism, the industrial platform organism Corynebacterium glutamicum is used. C. glutamicum uses a respiratory type of energy metabolism, implying that ATP can be synthesized either by SLP or by ETP with the membrane-bound F₁F_O-ATP synthase using the proton motive force (pmf) as driving force. The presence of two terminal oxidases, which differ in their proton translocation efficiency by a factor of three, further complicates energy balancing for this organism. By integration of experimental data and network models, we show that in the wild type SLP and ETP contribute equally to ATP generation. Thus, the role of ETP in respiring bacteria may have been overrated in the past. Remarkably, in the genome wide setting 65% of the pmf is actually not used for ATP synthesis. However, it turns out that, compared to other organisms C. glutamicum still uses its energy budget rather efficiently.

The Metano Modeling Toolbox MMTB: An Intuitive, Web-Based Toolbox Introduced by Two Use Cases

Genome-scale metabolic models are of high interest in a number of different research fields. Flux balance analysis (FBA) and other mathematical methods allow the prediction of the steady-state behavior of metabolic networks under different environmental conditions. However, many existing applications for flux optimizations do not provide a metabolite-centric view on fluxes. Metano is a standalone, open-source toolbox for the analysis and refinement of metabolic models. While flux distributions in metabolic networks are predominantly analyzed from a reaction-centric point of view, the Metano methods of split-ratio analysis and metabolite flux minimization also allow a metabolite-centric view on flux distributions. In addition, we present MMTB (Metano Modeling Toolbox), a web-based toolbox for metabolic modeling including a user-friendly interface to Metano methods. MMTB assists during bottom-up construction of metabolic models by integrating reaction and enzymatic annotation data from different databases. Furthermore, MMTB is especially designed for non-experienced users by providing an intuitive interface to the most commonly used modeling methods and offering novel visualizations. Additionally, MMTB allows users to upload their models, which can in turn be explored and analyzed by the community. We introduce MMTB by two use cases, involving a published model of Corynebacterium glutamicum and a newly created model of Phaeobacter inhibens.

Improvement of l-arginine production by in silico genome-scale metabolic network model guided genetic engineering

Genome-scale metabolic network model (GSMM) is an important in silico tool that can efficiently predict the target genes to be modulated. A Corynebacterium crenatum argB-M4 Cc_iKK446_arginine model was constructed on the basis of the GSMM of Corynebacterium glutamicum ATCC 13032 Cg_iKK446. Sixty-four gene deletion sites, twenty-four gene enhancement sites, and seven gene attenuation sites were determined for the improvement of l-arginine production in engineered C. crenatum. Among these sites, the effects of disrupting putP, cgl2310, pta, and Ncgl1221 and overexpressing lysE on l-arginine production were investigated. Moreover, the strain CCM007 with deleted putP, cgl2310, pta, and Ncgl1221 and overexpressed lysE produced 24.85 g/L l-arginine. This finding indicated a 106.8% improvement in l-arginine production compared with that in CCM01. GSMM is an excellent tool for identifying target genes to promote l-arginine accumulation in engineered C. crenatum.

Streamlined Pseudomonas taiwanensis VLB120 Chassis Strains with Improved Bioprocess Features

Microbes harbor many traits that are dispensable or even unfavorable under industrial and laboratory settings. The elimination of such traits could improve the host's efficiency, genetic stability, and robustness, thereby increasing the predictability and boosting its performance as a microbial cell factory. We engineered solvent-tolerant Pseudomonas taiwanensis VLB120 to yield streamlined chassis strains with higher growth rates and biomass yields, enhanced solvent tolerance, and improved process performance. In total, the genome was reduced by up to 10%. This was achieved by the elimination of genes that enable the cell to swim and form biofilms and by the deletion of the megaplasmid pSTY and large proviral segments. The resulting strain GRC1 had a 15% higher growth rate and biomass yield than the wildtype. However, this strain lacks the pSTY-encoded efflux pump TtgGHI, rendering it solvent-sensitive. Through reintegration of ttgGHI by chromosomal insertion without (GRC2) and with (GRC3) the corresponding regulator genes, the solvent-tolerant phenotype was enhanced. The generated P. taiwanensis GRC strains enlarge the repertoire of streamlined chassis with enhanced key performance indicators, making them attractive hosts for biotechnological applications. The different solvent tolerance levels of GRC1, GRC2, and GRC3 enable the selection of a fitting host platform in relation to the desired process requirements in a chassis à la carte principle. This was demonstrated in a metabolic engineering approach for the production of phenol from glycerol. The streamlined producer GRC1Δ5-TPL38 outperformed the equivalent nonstreamlined producer VLB120Δ5-TPL38 concerning phenol titer, rate, and yield, thereby highlighting the added value of the streamlined chassis.

Genome-Scale Metabolic Network Models of Bacillus Species Suggest that Model Improvement is Necessary for Biotechnological Applications

Background

A genome-scale metabolic network model (GEM) is a mathematical representation of an organism’s metabolism. Today, GEMs are popular tools for computationally simulating the biotechnological processes and for predicting biochemical properties of (engineered) strains.

Objectives

In the present study, we have evaluated the predictive power of two GEMs, namely iBsu1103 (for Bacillus subtilis 168) and iMZ1055 (for Bacillus megaterium WSH002).

Materials and Methods

For comparing the predictive power of Bacillus subtilis and Bacillus megaterium GEMs, experimental data were obtained from previous wet-lab studies included in PubMed. By using these data, we set the environmental, stoichiometric and thermodynamic constraints on the models, and FBA is performed to predict the biomass production rate, and the values of other fluxes. For simulating experimental conditions in this study, COBRA toolbox was used.

Results

By using the wealth of data in the literature, we evaluated the accuracy of in silico simulations of these GEMs. Our results suggest that there are some errors in these two models which make them unreliable for predicting the biochemical capabilities of these species. The inconsistencies between experimental and computational data are even greater where B. subtilis and B. megaterium do not have similar phenotypes.

Conclusions

Our analysis suggests that literature-based improvement of genome-scale metabolic network models of the two Bacillus species is essential if these models are to be successfully applied in biotechnology and metabolic engineering.

Predicting the Longitudinally and Radially Varying Gut Microbiota Composition Using Multi-Scale Microbial Metabolic Modeling

Background: The gut microbiota is a heterogeneous group of microbes that is spatially distributed along various sections of the intestines and across the mucosa and lumen in each section. Understanding the dynamics between the spatially differential microbial populations and the driving forces for the observed spatial organization will provide valuable insights into important questions such as the nature of colonization of the infant gut and different types of inflammatory bowel disease localized in different regions of the intestines. However, in most studies, the microbiota is sampled only at a single site (often feces) or from a particular anatomical site of the intestines. Differential oxygen availability is putatively a key factor shaping the spatial organization. Results: To test this hypothesis, we constructed a community genome-scale metabolic model consisting of representative organisms for the major phyla present in the human gut microbiome. By solving step-wise optimization problems embedded in a dynamic framework to predict community metabolism and integrate the mucosally-adherent with the luminal microbiome between consecutive sections along the intestines, we were able to capture (i) the essential features of the spatially differential composition of obligate anaerobes vs. facultative anaerobes and aerobes determined experimentally, and (ii) the accumulation of microbial biomass in the lumen. Sensitivity analysis suggests that the spatial organization depends primarily on the oxygen-per-microbe availability in each region. Oxygen availability is reduced relative to the ~100-fold increase in mucosal microbial density along the intestines, causing the switch between aerobes and anaerobes. Conclusion: The proposed integrated dynamic framework is able to predict spatially differential gut microbiota composition using microbial genome-scale metabolic models and test hypotheses regarding the dynamics of the gut microbiota. It can potentially become a valuable tool for exploring therapeutic strategies for site-specific perturbation of the gut microbiota and the associated metabolic activities.

Rationally designing antisense therapy to keep up with evolving bacterial resistance

Antisense molecules used as antibiotics offer the potential to keep up with acquired resistance, by redesigning the sequence of an antisense. Once bacteria acquire resistance by mutating the targeted sequence, new antisense can readily be designed by using sequence information of a target gene. However, antisense molecules require additional delivery vehicles to get into bacteria and be protected from degradation. Based on progress in the last few years it appears that, while redesigning or finding new delivery vehicle will be more difficult than redesigning the antisense cargo, it will perhaps be less difficult than finding new conventional small molecule antibiotics. In this study we propose a protocol that maximizes the combined advantages of engineered delivery vehicle and antisense cargo by decreasing the immediate growth advantage to the pathogen of mutating the entry mechanisms and increasing the advantage to the pathogen of antisense target mutations. Using this protocol, we show by computer simulation an appropriately designed antisense therapy can potentially be effective many times longer than conventional antibiotics before succumbing to resistance. While the simulations describe an in-vitro situation, based on comparison with other in-vitro studies on acquired resistance we believe the advantages of the combination antisense strategy have the potential to provide much more sustainability in vivo than conventional antibiotic therapy.

Deciphering bacterial xylose metabolism and metabolic engineering of industrial microorganisms for use as efficient microbial cell factories

The goal of sustainable production of biochemicals and biofuels has driven the engineering of microbial cell as factories that convert low-value substrates to high-value products. Xylose is the second most abundant sugar substrate in lignocellulosic hydrolysates. We analyzed the mechanisms of xylose metabolism using genome sequencing data of 492 industrially relevant bacterial species in the mini-review. The analysis revealed the xylose isomerase and Weimberg pathways as the major routes across diverse routes of bacterial xylose metabolism. In addition, we discuss recent developments in metabolic engineering of xylose metabolism in industrial microorganisms. Genome-scale analyses have revealed xylose pathway-specific flux landscapes. Overall, a comprehensive understanding of bacterial xylose metabolism could be useful for the feasible development of microbial cell factories.

Physiological Response of Corynebacterium glutamicum to Increasingly Nutrient-Rich Growth Conditions

To ensure economic competitiveness, bioprocesses should achieve maximum productivities enabled by high growth rates (μ) and equally high substrate consumption rates (qS) as a prerequisite of sufficient carbon-to-product conversion. Both traits were investigated and improved via bioprocess engineering approaches studying the industrial work horse Corynebacterium glutamicum. Standard minimal medium CGXII with glucose as sole carbon source was supplemented with complex brain-heart-infusion (BHI) or amino acid (AA) cocktails. Maximum μ of 0.67 h-1 was exclusively observed in 37 g BHI L-1 whereas only minor growth stimulation was found after AA supplementation (μ = 0.468 h-1). Increasing glucose consumption rates (qGlc) were solely observed in certain dosages of BHI (1-10 g L-1), while 37 g BHI L-1 and AA addition revealed qGlc below the reference experiments. Moreover, BHI supplementation revealed Monod-type saturation kinetics of μ (KBHI = 2.73 g BHI L-1) referring to the preference of non-AAs as key boosting nutrients. ATP-demands under reference, 1 g BHI L-1, and AA conditions were nearly constant but halved in BHI concentrations above 5 g L-1 reflecting the energetic advantage of consuming complex nutrient components in addition to "simple" building blocks such as AAs. Furthermore, C. glutamicum revealed maximum biomass per carbon yields of about 18 gCDW C-mol-1 irrespective of the medium. In AA supplementation experiments, simultaneous uptake of 17 AAs was observed, maximum individual consumption rates determined, and L-asparagine and L-glutamine were distinguished as compounds with the highest consumption rates. Employment of the expanded stoichiometric model iMG481 successfully reproduced experimental results and revealed the importance of C. glutamicum's transaminase network to compensate needs of limiting AA supply. Model-based sensitivity studies attributed the highest impact on μ to AAs with high ATP and NADPH demands such as L-tryptophan or L-phenylalanine.

A Genome-Scale Metabolic Reconstruction of Phytophthora infestans With the Integration of Transcriptional Data Reveals the Key Metabolic Patterns Involved in the Interaction of Its Host

Phytophthora infestans, the causal agent of late blight disease, affects potatoes and tomatoes worldwide. This plant pathogen has a hemibiotrophic lifestyle, having an initial biotrophic infection phase during which the pathogen spreads within the host tissue, followed by a necrotrophic phase in which host cell death is induced. Although increasing information is available on the molecular mechanisms, underlying the distinct phases of the hemibiotrophic lifestyle, studies that consider the entire metabolic processes in the pathogen while undergoing the biotrophic, transition to necrotrophic, and necrotrophic phases have not been conducted. In this study, the genome-scale metabolic reconstruction of P. infestans was achieved. Subsequently, transcriptional data (microarrays, RNA-seq) was integrated into the metabolic reconstruction to obtain context-specific (metabolic) models (CSMs) of the infection process, using constraint-based reconstruction and analysis. The goal was to identify specific metabolic markers for distinct stages of the pathogen's life cycle. Results indicate that the overall metabolism show significant changes during infection. The most significant changes in metabolism were observed at the latest time points of infection. Metabolic activity associated with purine, pyrimidine, fatty acid, fructose and mannose, arginine, glycine, serine, and threonine amino acids appeared to be the most important metabolisms of the pathogen during the course of the infection, showing high number of reactions associated with them and expression switches at important stages of the life cycle. This study provides a framework for future throughput studies of the metabolic changes during the hemibiotrophic life cycle of this important plant pathogen.

Mu-driven transposition of recombinant mini-Mu unit DNA in the Corynebacterium glutamicum chromosome

A dual-component Mu-transposition system was modified for the integration/amplification of genes in Corynebacterium. The system consists of two types of plasmids: (i) a non-replicative integrative plasmid that contains the transposing mini-Mu(LR) unit bracketed by the L/R Mu ends or the mini-Mu(LER) unit, which additionally contains the enhancer element, E, and (ii) an integration helper plasmid that expresses the transposition factor genes for MuA and MuB. Efficient transposition in the C. glutamicum chromosome (≈ 2 × 10⁻⁴ per cell) occurred mainly through the replicative pathway via cointegrate formation followed by possible resolution. Optimizing the E location in the mini-Mu unit significantly increased the efficiency of Mu-driven intramolecular transposition–amplification in C. glutamicum as well as in gram-negative bacteria. The new C. glutamicum genome modification strategy that was developed allows the consequent independent integration/amplification/fixation of target genes at high copy numbers. After integration/amplification of the first mini-Mu(LER) unit in the C. glutamicum chromosome, the E-element, which is bracketed by lox-like sites, is excised by Cre-mediated fashion, thereby fixing the truncated mini-Mu(LR) unit in its position for the subsequent integration/amplification of new mini-Mu(LER) units. This strategy was demonstrated using the genes for the citrine and green fluorescent proteins, yECitrine and yEGFP, respectively.

OptPipe - a pipeline for optimizing metabolic engineering targets

BACKGROUND

We propose OptPipe - a Pipeline for Optimizing Metabolic Engineering Targets, based on a consensus approach. The method generates consensus hypotheses for metabolic engineering applications by combining several optimization solutions obtained from distinct algorithms. The solutions are ranked according to several objectives, such as biomass and target production, by using the rank product tests corrected for multiple comparisons.

RESULTS

OptPipe was applied in a genome-scale model of Corynebacterium glutamicum for maximizing malonyl-CoA, which is a valuable precursor for many phenolic compounds. In vivo experimental validation confirmed increased malonyl-CoA level in case of ΔsdhCAB deletion, as predicted in silico.

CONCLUSIONS

A method was developed to combine the optimization solutions provided by common knockout prediction procedures and rank the suggested mutants according to the expected growth rate, production and a new adaptability measure. The implementation of the pipeline along with the complete documentation is freely available at https://github.com/AndrasHartmann/OptPipe .

1,5-Diaminopentane production from xylooligosaccharides using metabolically engineered Corynebacterium glutamicum displaying beta-xylosidase on the cell surface

Xylooligosaccharide-assimilating Corynebacterium glutamicum strains were constructed using metabolic engineering and cell surface display techniques. First, C. glutamicum was metabolically engineered to create lysine-producing strains. Beta-xylosidase BSU17580 derived from Bacillus subtilis was then expressed on the C. glutamicum cell surface using PorH anchor protein, and enzymes involved in the xylose assimilation pathway were also expressed. Metabolic engineering had no effect on the activity of beta-xylosidase. The engineered strains efficiently consumed xylooligosaccharides and produced 12.4mM of lysine from 11.9g/L of xylooligosaccharides as the carbon source. Finally, co-expression of lysine decarboxylase enabled production of 11.6mM of 1,5-diaminopentane (cadaverine) from 13g/L of consumed xylooligosaccharides.

Arginine deiminase pathway provides ATP and boosts growth of the gas-fermenting acetogen Clostridium autoethanogenum

Acetogens are attractive organisms for the production of chemicals and fuels from inexpensive and non-food feedstocks such as syngas (CO, CO₂ and H₂). Expanding their product spectrum beyond native compounds is dictated by energetics, particularly ATP availability. Acetogens have evolved sophisticated strategies to conserve energy from reduction potential differences between major redox couples, however, this coupling is sensitive to small changes in thermodynamic equilibria. To accelerate the development of strains for energy-intensive products from gases, we used a genome-scale metabolic model (GEM) to explore alternative ATP-generating pathways in the gas-fermenting acetogen Clostridium autoethanogenum. Shadow price analysis revealed a preference of C. autoethanogenum for nine amino acids. This prediction was experimentally confirmed under heterotrophic conditions. Subsequent in silico simulations identified arginine (ARG) as a key enhancer for growth. Predictions were experimentally validated, and faster growth was measured in media containing ARG (t_D~4h) compared to growth on yeast extract (t_D~9h). The growth-boosting effect of ARG was confirmed during autotrophic growth. Metabolic modelling and experiments showed that acetate production is nearly abolished and fast growth is realised by a three-fold increase in ATP production through the arginine deiminase (ADI) pathway. The involvement of the ADI pathway was confirmed by metabolomics and RNA-sequencing which revealed a ~500-fold up-regulation of the ADI pathway with an unexpected down-regulation of the Wood-Ljungdahl pathway. The data presented here offer a potential route for supplying cells with ATP, while demonstrating the usefulness of metabolic modelling for the discovery of native pathways for stimulating growth or enhancing energy availability.

A new genome-scale metabolic model of Corynebacterium glutamicum and its application

BACKGROUND

Corynebacterium glutamicum is an important platform organism for industrial biotechnology to produce amino acids, organic acids, bioplastic monomers, and biofuels. The metabolic flexibility, broad substrate spectrum, and fermentative robustness of C. glutamicum make this organism an ideal cell factory to manufacture desired products. With increases in gene function, transport system, and metabolic profile information under certain conditions, developing a comprehensive genome-scale metabolic model (GEM) of C. glutamicum ATCC13032 is desired to improve prediction accuracy, elucidate cellular metabolism, and guide metabolic engineering.

RESULTS

Here, we constructed a new GEM for ATCC13032, iCW773, consisting of 773 genes, 950 metabolites, and 1207 reactions. Compared to the previous model, iCW773 supplemented 496 gene-protein-reaction associations, refined five lumped reactions, balanced the mass and charge, and constrained the directionality of reactions. The simulated growth rates of C. glutamicum cultivated on seven different carbon sources using iCW773 were consistent with experimental values. Pearson's correlation coefficient between the iCW773-simulated and experimental fluxes was 0.99, suggesting that iCW773 provided an accurate intracellular flux distribution of the wild-type strain growing on glucose. Furthermore, genetic interventions for overproducing l-lysine, 1,2-propanediol and isobutanol simulated using OptForce_MUST were in accordance with reported experimental results, indicating the practicability of iCW773 for the design of metabolic networks to overproduce desired products. In vivo genetic modifications of iCW773-predicted targets resulted in the de novo generation of an l-proline-overproducing strain. In fed-batch culture, the engineered C. glutamicum strain produced 66.43 g/L l-proline in 60 h with a yield of 0.26 g/g (l-proline/glucose) and a productivity of 1.11 g/L/h. To our knowledge, this is the highest titer and productivity reported for l-proline production using glucose as the carbon resource in a minimal medium.

CONCLUSIONS

Our developed iCW773 serves as a high-quality platform for model-guided strain design to produce industrial bioproducts of interest. This new GEM will be a successful multidisciplinary tool and will make valuable contributions to metabolic engineering in academia and industry.

In silico design of anaerobic growth-coupled product formation in Escherichia coli: experimental validation using a simple polyol, glycerol

Integrated approaches using in silico model-based design and advanced genetic tools have enabled efficient production of fuels, chemicals and functional ingredients using microbial cell factories. In this study, using a recently developed genome-scale metabolic model for Escherichia coli iJO1366, a mutant strain has been designed in silico for the anaerobic growth-coupled production of a simple polyol, glycerol. Computational complexity was significantly reduced by systematically reducing the target reactions used for knockout simulations. One promising penta knockout E. coli mutant (E. coli ΔadhE ΔldhA ΔfrdC ΔtpiA ΔmgsA) was selected from simulation study and was constructed experimentally by sequentially deleting five genes. The penta mutant E. coli bearing the Saccharomyces cerevisiae glycerol production pathway was able to grow anaerobically and produce glycerol as the major metabolite with up to 90% of theoretical yield along with stoichiometric quantities of acetate and formate. Using the penta mutant E. coli strain we have demonstrated that the ATP formation from the acetate pathway was essential for growth under anaerobic conditions. The general workflow developed can be easily applied to anaerobic production of other platform chemicals using E. coli as the cell factory.

Elucidation of the regulatory role of the fructose operon reveals a novel target for enhancing the NADPH supply in Corynebacterium glutamicum

The performance of Corynebacterium glutamicum cell factories producing compounds which rely heavily on NADPH has been reported to depend on the sugar being metabolized. While some aspects of this phenomenon have been elucidated, there are still many unresolved questions as to how sugar metabolism is linked to redox and to the general metabolism. We here provide new insights into the regulation of the metabolism of this important platform organism by systematically characterizing mutants carrying various lesions in the fructose operon. Initially, we found that a strain where the dedicated fructose uptake system had been inactivated (KO-ptsF) was hampered in growth on sucrose minimal medium, and suppressor mutants appeared readily. Comparative genomic analysis in conjunction with enzymatic assays revealed that suppression was linked to inactivation of the pfkB gene, encoding a fructose-1-phosphate kinase. Detailed characterization of KO-ptsF, KO-pfkB and double knock-out (DKO) derivatives revealed a strong role for sugar-phosphates, especially fructose-1-phosphate (F1P), in governing sugar as well as redox metabolism due to effects on transcriptional regulation of key genes. These findings allowed us to propose a simple model explaining the correlation between sugar phosphate concentration, gene expression and ultimately the observed phenotype. To guide us in our analysis and help us identify bottlenecks in metabolism we debugged an existing genome-scale model onto which we overlaid the transcriptome data. Based on the results obtained we managed to enhance the NADPH supply and transform the wild-type strain into delivering the highest yield of lysine ever obtained on sucrose and fructose, thus providing a good example of how regulatory mechanisms can be harnessed for bioproduction.

Phylogenomic Analysis of Natural Products Biosynthetic Gene Clusters Allows Discovery of Arseno-Organic Metabolites in Model Streptomycetes

Natural products from microbes have provided humans with beneficial antibiotics for millennia. However, a decline in the pace of antibiotic discovery exerts pressure on human health as antibiotic resistance spreads, a challenge that may better faced by unveiling chemical diversity produced by microbes. Current microbial genome mining approaches have revitalized research into antibiotics, but the empirical nature of these methods limits the chemical space that is explored.

Here, we address the problem of finding novel pathways by incorporating evolutionary principles into genome mining. We recapitulated the evolutionary history of twenty-three enzyme families previously uninvestigated in the context of natural product biosynthesis in Actinobacteria, the most proficient producers of natural products. Our genome evolutionary analyses where based on the assumption that expanded—repurposed enzyme families—from central metabolism, occur frequently and thus have the potential to catalyze new conversions in the context of natural products biosynthesis. Our analyses led to the discovery of biosynthetic gene clusters coding for hidden chemical diversity, as validated by comparing our predictions with those from state-of-the-art genome mining tools; as well as experimentally demonstrating the existence of a biosynthetic pathway for arseno-organic metabolites in Streptomyces coelicolor and Streptomyces lividans, Using a gene knockout and metabolite profile combined strategy.

As our approach does not rely solely on sequence similarity searches of previously identified biosynthetic enzymes, these results establish the basis for the development of an evolutionary-driven genome mining tool termed EvoMining that complements current platforms. We anticipate that by doing so real ‘chemical dark matter’ will be unveiled.

Glutamate Fermentation-2: Mechanism of L-Glutamate Overproduction in Corynebacterium glutamicum

The nonpathogenic coryneform bacterium, Corynebacterium glutamicum, was isolated as an L-glutamate-overproducing microorganism by Japanese researchers and is currently utilized in various amino acid fermentation processes. L-Glutamate production by C. glutamicum is induced by limitation of biotin and addition of fatty acid ester surfactants and β-lactam antibiotics. These treatments affect the cell surface structures of C. glutamicum. After the discovery of C. glutamicum, many researchers have investigated the underlying mechanism of L-glutamate overproduction with respect to the cell surface structures of this organism. Furthermore, metabolic regulation during L-glutamate overproduction by C. glutamicum, particularly, the relationship between central carbon metabolism and L-glutamate biosynthesis, has been investigated. Recently, the role of a mechanosensitive channel protein in L-glutamate overproduction has been reported. In this chapter, mechanisms of L-glutamate overproduction by C. glutamicum have been reviewed.

Reconstruction and analysis of a genome-scale metabolic network of Corynebacterium glutamicum S9114

Corynebacterium glutamicum S9114 is commonly used for industrial glutamate production. Therefore, a comprehensive understanding of the physiological and metabolic characteristics of C. glutamicum is important for developing its potential for industrial production. A genome-scale metabolic model, iJM658, was reconstructed based on genome annotation and literature mining. The model consists of 658 genes, 984 metabolites and 1065 reactions. The model quantitatively predicted C. glutamicum growth on different carbon and nitrogen sources and determined 129 genes to be essential for cell growth. The iJM658 model predicted that C. glutamicum had two glutamate biosynthesis pathways and lacked eight key genes in biotin synthesis. Robustness analysis indicated a relative low oxygen level (1.21mmol/gDW/h) would improve glutamate production rate. Potential metabolic engineering targets for improving γ-aminobutyrate and isoleucine production rate were predicted by in silico deletion or overexpression of some genes. The iJM658 model is a useful tool for understanding and optimizing the metabolism of C. glutamicum and a valuable resource for future metabolic and physiological research.

Advancing metabolic engineering through systems biology of industrial microorganisms

Development of sustainable processes to produce bio-based compounds is necessary due to the severe environmental problems caused by the use of fossil resources. Metabolic engineering can facilitate the development of highly efficient cell factories to produce these compounds from renewable resources. The objective of systems biology is to gain a comprehensive and quantitative understanding of living cells and can hereby enhance our ability to characterize and predict cellular behavior. Systems biology of industrial microorganisms is therefore valuable for metabolic engineering. Here we review the application of systems biology tools for the identification of metabolic engineering targets which may lead to reduced development time for efficient cell factories. Finally, we present some perspectives of systems biology for advancing metabolic engineering further.

Critical assessment of genome-scale metabolic networks: the need for a unified standard

Genome-scale metabolic networks have been reconstructed for several organisms. These metabolic networks provide detailed information about the metabolism inside the cells, coupled with the genomic, proteomic and thermodynamic information. These networks are widely simulated using 'constraint-based' modelling techniques and find applications ranging from strain improvement for metabolic engineering to prediction of drug targets in pathogenic organisms. Components of these metabolic networks are represented in multiple file formats and also using different markup languages, with varying levels of annotations; this leads to inconsistencies and increases the complexities in comparing and analysing reconstructions on multiple platforms. In this work, we critically examine nearly 100 published genome-scale metabolic networks and their corresponding constraint-based models and discuss various issues with respect to model quality. One of the major concerns is the lack of annotations using standard identifiers that can uniquely describe several components such as metabolites, genes, proteins and reactions. We also find that many models do not have complete information regarding constraints on reactions fluxes and objective functions for carrying out simulations. Overall, our analysis highlights the need for a widely acceptable standard for representing constraint-based models. A rigorous standard can help in streamlining the process of reconstruction and improve the quality of reconstructed metabolic models.