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

Engineering Corynebacterium glutamicum for efficient l-homoserine production

l-homoserine is an important precursor in synthesizing the essential amino acids derived from l-aspartate and other valuable bio-based products, and is widely used in cosmetics, food, and pharmaceutical industries. However, the yield of l-homoserine in Corynebacterium glutamicum is limited. In this study, we successfully engineered a C. glutamicum to efficiently produce l-homoserine. First, a l-threonine-producing strain TWZ023 was optimized by knocking out thrB, resulting in the strain HWZ006 which could produce 20.8 g/L l-homoserine with a yield of 0.231 g/g glucose. Then, the 240th Arg residue of homoserine kinase (HK) in TWZ023 was identified as an important site for l-homoserine binding. Subsequently, the optimal mutant strain R240I was screened through site-directed saturation mutagenesis of HK in TWZ023, it could produce 26.8 g/L l-homoserine with a yield of 0.298 g/g glucose. The HK-homoserine binding mechanism was analyzed by using molecular docking, further providing some potential mutation sites. The final strain R240I/pXTuf-Cgl2344 was obtained by optimizing the efflux of l-homoserine, it could produce 29.9 g/L l-homoserine with a yield of 0.332 g/g glucose in shake flasks. In a 15 L fermenter, the final strain produced 78.3 g/L with a yield of 0.28 g/g glucose. These metabolic engineering strategies used in this study have fundamentally enhanced the ability of C. glutamicum to produce l-homoserine.

CarveAdornCurate: a versatile cloud-based platform for constructing multiscale metabolic models

Multiscale modeling is a promising approach for understanding cellular behaviors. However, existing multiscale modeling tools require meticulously curated genome-scale metabolic models (GEMs) as inputs, limiting the broad applications of multiscale models due to complex and time-consuming construction processes. To this end, we developed a novel workflow named CarveAdornCurate (CAC) for de novo multiscale modeling. The Carve module generates an ensemble of GEMs with strong genetic evidence, which is then upgraded to multiscale models using Adorn module. The Curate module was designed to find features important to the generated models. These three modules are integrated into a cloud-based platform to promote broad accessibility. As proof of concept, we constructed CAC-based multiscale models for Corynebacterium glutamicum and Yarrowia lipolytica, demonstrating their potential in guiding metabolic engineering. Overall, CAC is demonstrated to be an efficient and user-friendly tool for constructing multiscale models. It is available online at www.carveadorncurate.com/.

Comprehensive evaluation of the capacities of microbial cell factories

Systems metabolic engineering is facilitating the development of high-performing microbial cell factories for producing chemicals and materials. However, constructing an efficient microbial cell factory still requires exploring and selecting various host strains, as well as identifying the best-suited metabolic engineering strategies, which demand significant time, effort, and costs. Here, we comprehensively evaluate the capacities of various microbial cell factories and propose strategies for systems metabolic engineering steps, including host strain selection, metabolic pathway reconstruction, and metabolic flux optimization. We analyze the metabolic capacities of five representative industrial microorganisms as cell factories for the production of 235 different bio-based chemicals and suggest the most suitable host strain for the corresponding chemical production. To improve the innate metabolic capacity by constructing more efficient metabolic pathways, heterologous metabolic reactions, and cofactor exchanges are systematically analyzed. Additionally, we present metabolic engineering strategies, which include up- and down-regulation target reactions, for the improved production of chemicals. Altogether, this study will serve as a comprehensive resource for the systems metabolic engineering of microorganisms in the bio-based production of chemicals.

Recent advances in the Design-Build-Test-Learn (DBTL) cycle for systems metabolic engineering of Corynebacterium glutamicum

Existing microbial engineering strategies-encompassing metabolic engineering, systems biology, and systems metabolic engineering-have significantly enhanced the potential of microbial cell factories as sustainable alternatives to the petrochemical industry by optimizing metabolic pathways. Recently, systems metabolic engineering, which integrates tools from synthetic biology, enzyme engineering, omics technology, and evolutionary engineering, has been successfully developed. By leveraging modern engineering strategies within the Design-Build-Test-Learn (DBTL) cycle framework, these advancements have revolutionized the biosynthesis of valuable compounds. This review highlights recent progress in the metabolic engineering of Corynebacterium glutamicum, a versatile microbial platform, achieved through various approaches from traditional metabolic engineering to advanced systems metabolic engineering, all within the DBTL cycle. A particular focus is placed C5 platform chemicals derived from L-lysine, one of the key amino acid production pathways of C. glutamicum. The development of DBTL cycle-based metabolic engineering strategies for this process is discussed.

Transcriptional profiling reveals the effect of arginine on Actinobacillus succinogenes growth and fermentation

Actinobacillus succinogenes is considered one of the most promising strains for succinic acid production due to its ability to utilize various carbon sources for high-concentration fermentation. However, limited understanding of genetic and metabolic changes during its growth and fermentation processes hinders further modification of A. succinogenes for application in SA production. In this study, we analyzed transcriptome profiles during A. succinogenes fermentation using high-throughput RNA sequencing. Compared to cells after 8 h of fermentation, cells at 34 h exhibited significant upregulation of genes related to glycerophospholipid metabolism and arginine biosynthesis pathways. We explored the effects of arginine on cell growth and fermentation by overexpressing or knocking down argH encoding argininosuccinate lyase (ArgH), a key enzyme in arginine biosynthesis. The integrity of the arginine metabolic pathway is essential for normal growth, and both exogenous addition of arginine and increased intracellular arginine metabolic flux improved cell growth and SA yield at low pH (5.0 ≤ pH ≤ 6.0). However, at non-low pH (6.0 ≤ pH ≤ 7.0), arginine had no significant effect on cell growth and SA yield. In a 3 L bioreactor under non-low pH conditions, the overexpression strain (::ArgH) produced 73.9 g/L SA with 1.65 g/L/h productivity, similar to the control strain. This implies that arginine metabolism in A. succinogenes is more associated with resistance to acid stress and less closely related to direct SA production. These findings provide insight into the critical physiological and biochemical processes of this non-model microorganism.

Metabolic Engineering of Corynebacterium glutamicum for the Production of the Four-Carbon Platform Chemicals γ-Hydroxybutyrate and γ-Butyrolactone

γ-Hydroxybutyrate (GHB) is an important C4 platform chemical, serving as a crucial precursor for the synthesis of various bulk chemicals, including γ-butyrolactone (GBL) and 1,4-butanediol (1,4-BDO). In this study, we report the systematic metabolic engineering of Corynebacterium glutamicum for the biological production of GHB from glucose via the introduction of a glutamate-derived pathway. We showed that C. glutamicum is a promising host for producing GHB due to its higher tolerance to GHB as compared to other chassis. By screening key enzymes capable of converting glutamate into GHB and blocking byproduct synthesis pathways, an engineered C. glutamicum strain was developed that achieved a GHB production titer of 30.6 g/L. Comparative transcriptome analysis was subsequently employed to identify previously uncharacterized aldehyde dehydrogenases responsible for succinate accumulation, and knockout of the corresponding genes led to an increased GHB titer of 33.7 g/L. Ultimately, the integration of a phosphoketolase-mediated nonoxidative glycolysis (NOG) pathway further enhanced GHB production, resulting in an accumulation of 38.3 g/L of GHB with a yield of 0.615 mol/mol glucose during batch fermentation. The GHB in the fermentation broth can be efficiently converted into GBL by acid treatment with a yield of 0.970 mol/mol.

Harnessing the optimization of enzyme catalytic rates in engineering of metabolic phenotypes

The increasing availability of enzyme turnover number measurements from experiments and of turnover number predictions from deep learning models prompts the use of these enzyme parameters in precise metabolic engineering. Yet, there is no computational approach that allows the prediction of metabolic engineering strategies that rely on the modification of turnover numbers. It is also unclear if modifications of turnover numbers without alterations in the host's transcriptional regulatory machinery suffice to increase the production of chemicals of interest. Here, we present a constraint-based modeling approach, termed Overcoming Kinetic rate Obstacles (OKO), that uses enzyme-constrained metabolic models to predict in silico strategies to increase the production of a given chemical, while ensuring specified cell growth. We demonstrate that the application of OKO to enzyme-constrained metabolic models of Escherichia coli and Saccharomyces cerevisiae results in strategies that can at least double the production of over 40 compounds with little penalty to growth. Interestingly, we show that the overproduction of compounds of interest does not entail only an increase in the values of turnover numbers. Lastly, we demonstrate that a refinement of OKO, allowing also for manipulation of enzyme abundance, facilitates the usage of the available compendia and deep learning models of turnover numbers in the design of precise metabolic engineering strategies. Our results expand the usage of genome-scale metabolic models toward the identification of targets for protein engineering, allowing their direct usage in the generation of innovative metabolic engineering designs for various biotechnological applications.

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.

Model-guided metabolic rewiring to bypass pyruvate oxidation for pyruvate derivative synthesis by minimizing carbon loss

Engineering microbial hosts to synthesize pyruvate derivatives depends on blocking pyruvate oxidation, thereby causing severe growth defects in aerobic glucose-based bioprocesses. To decouple pyruvate metabolism from cell growth to improve pyruvate availability, a genome-scale metabolic model combined with constraint-based flux balance analysis, geometric flux balance analysis, and flux variable analysis was used to identify genetic targets for strain design. Using translation elements from a ~3,000 cistronic library to modulate fxpK expression in a bicistronic cassette, a bifido shunt pathway was introduced to generate three molecules of non-pyruvate-derived acetyl-CoA from one molecule of glucose, bypassing pyruvate oxidation and carbon dioxide generation. The dynamic control of flux distribution by T7 RNAP-mediated synthetic small RNA decoupled pyruvate catabolism from cell growth. Adaptive laboratory evolution and multi-omics analysis revealed that a mutated isocitrate dehydrogenase functioned as a metabolic switch to activate the glyoxylate shunt as the only C4 anaplerotic pathway to generate malate from two molecules of acetyl-CoA input and bypass two decarboxylation reactions in the tricarboxylic acid cycle. A chassis strain for pyruvate derivative synthesis was constructed to reduce carbon loss by using the glyoxylate shunt as the only C4 anaplerotic pathway and the bifido shunt as a non-pyruvate-derived acetyl-CoA synthetic pathway and produced 22.46, 27.62, and 6.28 g/L of l-leucine, l-alanine, and l-valine by a controlled small RNA switch, respectively. Our study establishes a novel metabolic pattern of glucose-grown bacteria to minimize carbon loss under aerobic conditions and provides valuable insights into cell design for manufacturing pyruvate-derived products.IMPORTANCEBio-manufacturing from biomass-derived carbon sources using microbes as a cell factory provides an eco-friendly alternative to petrochemical-based processes. Pyruvate serves as a crucial building block for the biosynthesis of industrial chemicals; however, it is different to improve pyruvate availability in vivo due to the coupling of pyruvate-derived acetyl-CoA with microbial growth and energy metabolism via the oxidative tricarboxylic acid cycle. A genome-scale metabolic model combined with three algorithm analyses was used for strain design. Carbon metabolism was reprogrammed using two genetic control tools to fine-tune gene expression. Adaptive laboratory evolution and multi-omics analysis screened the growth-related regulatory targets beyond rational design. A novel metabolic pattern of glucose-grown bacteria is established to maintain growth fitness and minimize carbon loss under aerobic conditions for the synthesis of pyruvate-derived products. This study provides valuable insights into the design of a microbial cell factory for synthetic biology to produce industrial bio-products of interest.

Co-Cultivated Enzyme Constraint Metabolic Network Model for Rational Guidance in Constructing Synthetic Consortia to Achieve Optimal Pathway Allocation Prediction

Synthetic consortia have emerged as a promising biosynthetic platform that offers new opportunities for biosynthesis. Genome-scale metabolic network models (GEMs) with complex constraints are extensively utilized to guide the synthesis in monocultures. However, few methods are currently available to guide the rational construction of synthetic consortia for predicting the optimal allocation strategy of synthetic pathways aimed at enhancing product synthesis. A standardized method to construct the co-cultivated Enzyme Constraint metabolic network model (CulECpy) is proposed, which integrates enzyme constraints and modular interaction scale constraints based on the research concept of "independent + global". This method is applied to construct several synthetic consortia models, which encompassed different target products, strains, synthetic pathways, and compositional structures. Analyzing the model, the optimal pathway allocation and initial inoculum ratio that enhance the synthesis of target products by synthetic consortia are predicted and verified. When comparing with the constructed co-culture synthesis system, the normalized root mean square error of all optimal theoretical yield simulations is found to be less than or equal to 0.25. The analyses and verifications demonstrate that the method CulECpy can guide the rational construction of synthetic consortia systems to facilitate biochemical synthesis.

Rapid screening of point mutations by mismatch amplification mutation assay PCR

Metabolic engineering frequently makes use of point mutation and saturation mutation library creation. At present, sequencing is the only reliable and direct technique to detect point mutation and screen saturation mutation library. In this study, mismatch amplification mutation assay (MAMA) PCR was used to detect point mutation and screen saturation mutation library. In order to fine-tune the expression of odhA encoding 2-oxoglutarate dehydrogenase E1 component, a saturating mutant library of the RBS of odhA was created in Corynebacterium glutamicum P12 based on the CRISPR-Cas2a genome editing system, which increased the L-proline production by 81.3%. MAMA PCR was used to filter out 42% of the non-mutant transformants in the mutant library, which effectively reduced the workload of the subsequent fermentation test and the number of sequenced samples. The rapid and sensitive MAMA-PCR method established in this study provides a general strategy for detecting point mutations and improving the efficiency of mutation library screening. KEY POINTS: • MAMA PCR was optimized and developed to detect point mutation. • MAMA PCR greatly improves the screening efficiency of point mutation. • Attenuation of odhA expression in P12 effectively improves proline production.

Structure-based functional analysis of a novel NADPH-producing glyceraldehyde-3-phosphate dehydrogenase from Corynebacterium glutamicum

Corynebacterium glutamicum is an industrial workhorse applied in the production of valuable biochemicals. In the process of bio-based chemical production, improving cofactor recycling and mitigating cofactor imbalance are considered major solutions for enhancing the production yield and efficiency. Although, glyceraldehyde-3-phosphate dehydrogenase (GapDH), a glycolytic enzyme, can be a promising candidate for a sufficient NADPH cofactor supply, however, most microorganisms have only NAD-dependent GapDHs. In this study, we performed functional characterization and structure determination of novel NADPH-producing GapDH from C. glutamicum (CgGapX). Based on the crystal structure of CgGapX in complex with NADP cofactor, the unique structural features of CgGapX for NADP stabilization were elucidated. Also, N-terminal additional region (Auxiliary domain, AD) appears to have an effect on enzyme stabilization. In addition, through structure-guided enzyme engineering, we developed a CgGapX variant that exhibited 4.3-fold higher kcat, and 1.2-fold higher kcat/KM values when compared with wild-type. Furthermore, a bioinformatic analysis of 100 GapX-like enzymes from 97 microorganisms in the KEGG database revealed that the GapX-like enzymes possess a variety of AD, which seem to determine enzyme stability. Our findings are expected to provide valuable information for supplying NADPH cofactor pools in bio-based value-added chemical production.

Enhanced L-serine production by Corynebacterium glutamicum based on novel insights into L-serine exporters

The L-serine exporters ThrE and SerE play important roles in L-serine production by Corynebacterium glutamicum. Deletion of both thrE and serE decreased L-serine titer by 60%, suggesting the existence of other L-serine exporters. A comparative transcriptomics identified NCgl0254 and NCgl0255 as novel L-serine exporters. Further analysis of the contributions of ThrE, SerE, NCgl0254, and NCgl0255 found that SerE was the major L-serine exporter in C. glutamicum and these four L-serine exporters were responsible for 79.7% of L-serine export. Deletion of one L-serine exporter upregulated the transcription levels of the other three, which might be coursed by increased intracellular concentrations of L-serine. Overexpression of NCgl0254 and NCgl0255 increased L-serine titer by 20.8% in C. glutamicum A36, while overexpression of the four L-serine exporters increased L-serine production by 31.9% (41.1 g·L-1 ) in C. glutamicum A36. The identification of novel L-serine exporters in C. glutamicum will help to improve industrial production of L-serine.

Engineering Corynebacterium glutamicum for the efficient production of N-acetylglucosamine

N-acetylglucosamine (GlcNAc) is significant functional monosaccharides with diverse applications in medicine, food, and cosmetics. In this study, the GlcNAc synthesis pathway was constructed in Corynebacterium glutamicum and its reverse byproduct pathways were blocked. Simultaneously the driving force of GlcNAc synthesis was enhanced by screening key gene sources and inhibiting the GlcNAc consumption pathway. To maximize carbon flux, some competitive pathways (Pentose phosphate pathway, Glycolysis pathway and Mannose pathway) were weakened and the titer of GlcNAc reached 23.30 g/L in shake flasks. Through transcriptome analysis, it was found that dissolved oxygen was an important limiting factor, which was optimized in a 5 L bioreactor. Employing optimal fermentation conditions and feeding strategy, the titer of GlcNAc reached 138.9 g/L, with the yeild of 0.44 g/g glucose. This study significantly increased the yield and titer of GlcNAc, which lay a solid foundation for the industrial production of GlcNAc in C. glutamicum.

Characterization of the enzyme kinetics of EMP and HMP pathway in Corynebacterium glutamicum: reference for modeling metabolic networks

The model of intracellular metabolic network based on enzyme kinetics parameters plays an important role in understanding the intracellular metabolic process of Corynebacterium glutamicum, and constructing such a model requires a large number of enzymological parameters. In this work, the genes encoding the relevant enzymes of the EMP and HMP metabolic pathways from Corynebacterium glutamicum ATCC 13032 were cloned, and engineered strains for protein expression with E.coli BL21 and P.pastoris X33 as hosts were constructed. The twelve enzymes (GLK, GPI, TPI, GAPDH, PGK, PMGA, ENO, ZWF, RPI, RPE, TKT, and TAL) were successfully expressed and purified by Ni2+ chelate affinity chromatography in their active forms. In addition, the kinetic parameters (V max, K m, and K cat) of these enzymes were measured and calculated at the same pH and temperature. The kinetic parameters of enzymes associated with EMP and the HMP pathway were determined systematically and completely for the first time in C.glutamicum. These kinetic parameters enable the prediction of key enzymes and rate-limiting steps within the metabolic pathway, and support the construction of a metabolic network model for important metabolic pathways in C.glutamicum. Such analyses and models aid in understanding the metabolic behavior of the organism and can guide the efficient production of high-value chemicals using C.glutamicum as a host.

Elucidating the impact of in vitro cultivation on Nicotiana tabacum metabolism through combined in silico modeling and multiomics analysis

The systematical characterization and understanding of the metabolic behaviors are the basis of the efficient plant metabolic engineering and synthetic biology. Genome-scale metabolic networks (GSMNs) are indispensable tools for the comprehensive characterization of overall metabolic profile. Here we first constructed a GSMN of tobacco, which is one of the most widely used plant chassis, and then combined the tobacco GSMN and multiomics analysis to systematically elucidate the impact of in-vitro cultivation on the tobacco metabolic network. In-vitro cultivation is a widely used technique for plant cultivation, not only in the field of basic research but also for the rapid propagation of valuable horticultural and pharmaceutical plants. However, the systemic effects of in-vitro cultivation on overall plant metabolism could easily be overlooked and are still poorly understood. We found that in-vitro tobacco showed slower growth, less biomass and suppressed photosynthesis than soil-grown tobacco. Many changes of metabolites and metabolic pathways between in-vitro and soil-grown tobacco plants were identified, which notably revealed a significant increase of the amino acids content under in-vitro condition. The in silico investigation showed that in-vitro tobacco downregulated photosynthesis and primary carbon metabolism, while significantly upregulated the GS/GOGAT cycle, as well as producing more energy and less NADH/NADPH to acclimate in-vitro growth demands. Altogether, the combination of experimental and in silico analyses offers an unprecedented view of tobacco metabolism, with valuable insights into the impact of in-vitro cultivation, enabling more efficient utilization of in-vitro techniques for plant propagation and metabolic engineering.

Can Genome Sequencing Coupled to Flux Balance Analyses Offer Precision Guidance for Industrial Strain Development? The Lessons from Carbon Trafficking in Corynebacterium glutamicum ATCC 21573

Systems biology tools offer new prospects for industrial strain selection. For bacteria that are significant for industrial applications, whole-genome sequencing coupled to flux balance analysis (FBA) can help unpack the complex relationships between genome mutations and carbon trafficking. This work investigates the l-tyrosine (l-Tyr) overproducing model system Corynebacterium glutamicum ATCC 21573 with an eye to more rational and precision strain development. Using genome-wide mutational analysis of C. glutamicum, we identified 27,611 single nucleotide polymorphisms and 479 insertion/deletion mutations. Mutations in the carbon uptake machinery have led to phosphotransferase system-independent routes as corroborated with FBA. Mutations within the central carbon metabolism of C. glutamicum impaired the carbon flux, as evidenced by the lower growth rate. The entry to and flow through the tricarboxylic acid cycle was affected by mutations in pyruvate and α-ketoglutarate dehydrogenase complexes, citrate synthase, and isocitrate dehydrogenase. FBA indicated that the estimated flux through the shikimate pathway became larger as the l-Tyr production rate increased. In addition, protocatechuate export was probabilistically impossible, which could have contributed to the l-Tyr accumulation. Interestingly, aroG and cg0975, which have received previous attention for aromatic amino acid overproduction, were not mutated. From the branch point molecule, prephenate, the change in the promoter region of pheA could be an influential contributor. In summary, we suggest that genome sequencing coupled with FBA is well poised to offer rational guidance for industrial strain development, as evidenced by these findings on carbon trafficking in C. glutamicum ATCC 21573.

Automating the design-build-test-learn cycle towards next-generation bacterial cell factories

Automation is playing an increasingly significant role in synthetic biology. Groundbreaking technologies, developed over the past 20 years, have enormously accelerated the construction of efficient microbial cell factories. Integrating state-of-the-art tools (e.g. for genome engineering and analytical techniques) into the design-build-test-learn cycle (DBTLc) will shift the metabolic engineering paradigm from an almost artisanal labor towards a fully automated workflow. Here, we provide a perspective on how a fully automated DBTLc could be harnessed to construct the next-generation bacterial cell factories in a fast, high-throughput fashion. Innovative toolsets and approaches that pushed the boundaries in each segment of the cycle are reviewed to this end. We also present the most recent efforts on automation of the DBTLc, which heralds a fully autonomous pipeline for synthetic biology in the near future.

Kluyveromyces as promising yeast cell factories for industrial bioproduction: From bio-functional design to applications

As the two most widely used Kluyveromyces yeast, Kluyveromyces marxianus and K. lactis have gained increasing attention as microbial chassis in biocatalysts, biomanufacturing and the utilization of low-cost raw materials owing to their high suitability to these applications. However, due to slow progress in the development of molecular genetic manipulation tools and synthetic biology strategies, Kluyveromyces yeast cell factories as biological manufacturing platforms have not been fully developed. In this review, we provide a comprehensive overview of the attractive characteristics and applications of Kluyveromyces cell factories, with special emphasis on the development of molecular genetic manipulation tools and systems engineering strategies for synthetic biology. In addition, future avenues in the development of Kluyveromyces cell factories for the utilization of simple carbon compounds as substrates, the dynamic regulation of metabolic pathways, and for rapid directed evolution of robust strains are proposed. We expect that more synthetic systems, synthetic biology tools and metabolic engineering strategies will adapt to and optimize for Kluyveromyces cell factories to achieve green biofabrication of multiple products with higher efficiency.

Systems biology elucidates the distinctive metabolic niche filled by the human gut microbe Eggerthella lenta

Human gut bacteria perform diverse metabolic functions with consequences for host health. The prevalent and disease-linked Actinobacterium Eggerthella lenta performs several unusual chemical transformations, but it does not metabolize sugars and its core growth strategy remains unclear. To obtain a comprehensive view of the metabolic network of E. lenta, we generated several complementary resources: defined culture media, metabolomics profiles of strain isolates, and a curated genome-scale metabolic reconstruction. Stable isotope-resolved metabolomics revealed that E. lenta uses acetate as a key carbon source while catabolizing arginine to generate ATP, traits which could be recapitulated in silico by our updated metabolic model. We compared these in vitro findings with metabolite shifts observed in E. lenta-colonized gnotobiotic mice, identifying shared signatures across environments and highlighting catabolism of the host signaling metabolite agmatine as an alternative energy pathway. Together, our results elucidate a distinctive metabolic niche filled by E. lenta in the gut ecosystem. Our culture media formulations, atlas of metabolomics data, and genome-scale metabolic reconstructions form a freely available collection of resources to support further study of the biology of this prevalent gut bacterium.

Machine learning for metabolic pathway optimization: A review

Optimizing the metabolic pathways of microbial cell factories is essential for establishing viable biotechnological production processes. However, due to the limited understanding of the complex setup of cellular machinery, building efficient microbial cell factories remains tedious and time-consuming. Machine learning (ML), a powerful tool capable of identifying patterns within large datasets, has been used to analyze biological datasets generated using various high-throughput technologies to build data-driven models for complex bioprocesses. In addition, ML can also be integrated with Design-Build-Test-Learn to accelerate development. This review focuses on recent ML applications in genome-scale metabolic model construction, multistep pathway optimization, rate-limiting enzyme engineering, and gene regulatory element designing. In addition, we have discussed some limitations of these methods as well as potential solutions.

Systematizing Microbial Bioplastic Production for Developing Sustainable Bioeconomy: Metabolic Nexus Modeling, Economic and Environmental Technologies Assessment

The excessive usage of non-renewable resources to produce plastic commodities has incongruously influenced the environment's health. Especially in the times of COVID-19, the need for plastic-based health products has increased predominantly. Given the rise in global warming and greenhouse gas emissions, the lifecycle of plastic has been established to contribute to it significantly. Bioplastics such as polyhydroxy alkanoates, polylactic acid, etc. derived from renewable energy origin have been a magnificent alternative to conventional plastics and reconnoitered exclusively for combating the environmental footprint of petrochemical plastic. However, the economically reasonable and environmentally friendly procedure of microbial bioplastic production has been a hard nut to crack due to less scouted and inefficient process optimization and downstream processing methodologies. Thereby, meticulous employment of computational tools such as genome-scale metabolic modeling and flux balance analysis has been practiced in recent times to understand the effect of genomic and environmental perturbations on the phenotype of the microorganism. In-silico results not only aid us in determining the biorefinery abilities of the model microorganism but also curb our reliance on equipment, raw materials, and capital investment for optimizing the best conditions. Additionally, to accomplish sustainable large-scale production of microbial bioplastic in a circular bioeconomy, extraction, and refinement of bioplastic needs to be investigated extensively by practicing techno-economic analysis and life cycle assessment. This review put forth state-of-the-art know-how on the proficiency of these computational techniques in laying the foundation of an efficient bioplastic manufacturing blueprint, chiefly focusing on microbial polyhydroxy alkanoates (PHA) production and its efficacy in outplacing fossil based plastic products.

Biosystem design of Corynebacterium glutamicum for bioproduction

Corynebacterium glutamicum, a natural glutamate-producing bacterium adopted for industrial production of amino acids, has been extensively explored recently for high-level biosynthesis of amino acid derivatives, bulk chemicals such as organic acids and short-chain alcohols, aromatics, and natural products, including polyphenols and terpenoids. Here, we review the recent advances with a focus on biosystem design principles, metabolic characterization and modeling, omics analysis, utilization of nonmodel feedstock, emerging CRISPR (Clustered Regularly Interspaced Short Palindromic Repeats) tools for Corynebacterium strain engineering, biosensors, and novel strains of C. glutamicum. Future research directions for developing C. glutamicum cell factories are also discussed.

Construction and Analysis of an Enzyme-Constrained Metabolic Model of Corynebacterium glutamicum

The genome-scale metabolic model (GEM) is a powerful tool for interpreting and predicting cellular phenotypes under various environmental and genetic perturbations. However, GEM only considers stoichiometric constraints, and the simulated growth and product yield values will show a monotonic linear increase with increasing substrate uptake rate, which deviates from the experimentally measured values. Recently, the integration of enzymatic constraints into stoichiometry-based GEMs was proven to be effective in making novel discoveries and predicting new engineering targets. Here, we present the first genome-scale enzyme-constrained model (ecCGL1) for Corynebacterium glutamicum reconstructed by integrating enzyme kinetic data from various sources using a ECMpy workflow based on the high-quality GEM of C. glutamicum (obtained by modifying the iCW773 model). The enzyme-constrained model improved the prediction of phenotypes and simulated overflow metabolism, while also recapitulating the trade-off between biomass yield and enzyme usage efficiency. Finally, we used the ecCGL1 to identify several gene modification targets for l-lysine production, most of which agree with previously reported genes. This study shows that incorporating enzyme kinetic information into the GEM enhances the cellular phenotypes prediction of C. glutamicum, which can help identify key enzymes and thus provide reliable guidance for metabolic engineering.

Designing Microbial Cell Factories for the Production of Chemicals

The sustainable production of chemicals from renewable, nonedible biomass has emerged as an essential alternative to address pressing environmental issues arising from our heavy dependence on fossil resources. Microbial cell factories are engineered microorganisms harboring biosynthetic pathways streamlined to produce chemicals of interests from renewable carbon sources. The biosynthetic pathways for the production of chemicals can be defined into three categories with reference to the microbial host selected for engineering: native-existing pathways, nonnative-existing pathways, and nonnative-created pathways. Recent trends in leveraging native-existing pathways, discovering nonnative-existing pathways, and designing de novo pathways (as nonnative-created pathways) are discussed in this Perspective. We highlight key approaches and successful case studies that exemplify these concepts. Once these pathways are designed and constructed in the microbial cell factory, systems metabolic engineering strategies can be used to improve the performance of the strain to meet industrial production standards. In the second part of the Perspective, current trends in design tools and strategies for systems metabolic engineering are discussed with an eye toward the future. Finally, we survey current and future challenges that need to be addressed to advance microbial cell factories for the sustainable production of chemicals.

Model-Guided Metabolic Rewiring for Gamma-Aminobutyric Acid and Butyrolactam Biosynthesis in Corynebacterium glutamicum ATCC13032

Gamma-aminobutyric acid (GABA) can be used as a bioactive component in the pharmaceutical industry and a precursor for the synthesis of butyrolactam, which functions as a monomer for the synthesis of polyamide 4 (nylon 4) with improved thermal stability and high biodegradability. The bio-based fermentation production of chemicals using microbes as a cell factory provides an alternative to replace petrochemical-based processes. Here, we performed model-guided metabolic engineering of Corynebacterium glutamicum for GABA and butyrolactam fermentation. A GABA biosynthetic pathway was constructed using a bi-cistronic expression cassette containing mutant glutamate decarboxylase. An in silico simulation showed that the increase in the flux from acetyl-CoA to α-ketoglutarate and the decrease in the flux from α-ketoglutarate to succinate drove more flux toward GABA biosynthesis. The TCA cycle was reconstructed by increasing the expression of acn and icd genes and deleting the sucCD gene. Blocking GABA catabolism and rewiring the transport system of GABA further improved GABA production. An acetyl-CoA-dependent pathway for in vivo butyrolactam biosynthesis was constructed by overexpressing act-encoding ß-alanine CoA transferase. In fed-batch fermentation, the engineered strains produced 23.07 g/L of GABA with a yield of 0.52 mol/mol from glucose and 4.58 g/L of butyrolactam. The metabolic engineering strategies can be used for genetic modification of industrial strains to produce target chemicals from α-ketoglutarate as a precursor, and the engineered strains will be useful to synthesize the bio-based monomer of polyamide 4 from renewable resources.

Fermentative Indole Production via Bacterial Tryptophan Synthase Alpha Subunit and Plant Indole-3-Glycerol Phosphate Lyase Enzymes

Indole is produced in nature by diverse organisms and exhibits a characteristic odor described as animal, fecal, and floral. In addition, it contributes to the flavor in foods, and it is applied in the fragrance and flavor industry. In nature, indole is synthesized either from tryptophan by bacterial tryptophanases (TNAs) or from indole-3-glycerol phosphate (IGP) by plant indole-3-glycerol phosphate lyases (IGLs). While it is widely accepted that the tryptophan synthase α-subunit (TSA) has intrinsically low IGL activity in the absence of the tryptophan synthase β-subunit, in this study, we show that Corynebacterium glutamicum TSA functions as a bona fide IGL and can support fermentative indole production in strains providing IGP. By bioprospecting additional bacterial TSAs and plant IGLs that function as bona fide IGLs were identified. Capturing indole in an overlay enabled indole production to titers of about 0.7 g L^-1 in fermentations using C. glutamicum strains expressing either the endogenous TSA gene or the IGL gene from wheat.

Improved the lipopeptide production of Bacillus amyloliquefaciens HM618 under co-culture with the recombinant Corynebacterium glutamicum producing high-level proline

The application of antibacterial lipopeptides is limited by high cost and low yield. Herein, the exogenous L-proline significantly improved lipopeptide production by Bacillus amyloliquefaciens HM618. A recombinant Corynebacterium glutamicum producing high levels of proline using genetically modifying proB and putA was used to establish consortium, to improve lipopeptide production of strain HM618. Compared to a pure culture, the levels of iturin A, fengycin, and surfactin in consortium reached 67.75, 39.32, and 37.25 mg L^-1, respectively, an increase of 3.19-, 2.05-, and 1.63-fold over that produced by co-cultures of B. amyloliquefaciens and recombinant C. glutamicum with normal medium. Commercial amylase and recombinant Pichia pastoris with a heterologous amylase gene were used to hydrolyze kitchen waste. A three-strain consortium with recombinant P. pastoris and C. glutamicum increased the lipopeptide production of strain HM618 in medium containing KW. This work provides new strategies to improve lipopeptide production by B. amyloliquefaciens.

Production of indole by Corynebacterium glutamicum microbial cell factories for flavor and fragrance applications

Background

The nitrogen containing aromatic compound indole is known for its floral odor typical of jasmine blossoms. Due to its characteristic scent, it is frequently used in dairy products, tea drinks and fine fragrances. The demand for natural indole by the flavor and fragrance industry is high, yet, its abundance in essential oils isolated from plants such as jasmine and narcissus is low. Thus, there is a strong demand for a sustainable method to produce food-grade indole.

Results

Here, we established the biotechnological production of indole upon l-tryptophan supplementation in the bacterial host Corynebacterium glutamicum. Heterologous expression of the tryptophanase gene from E. coli enabled the conversion of supplemented l-tryptophan to indole. Engineering of the substrate import by co-expression of the native aromatic amino acid permease gene aroP increased whole-cell biotransformation of l-tryptophan to indole by two-fold. Indole production to 0.2 g L⁻¹ was achieved upon feeding of 1 g L⁻¹l-tryptophan in a bioreactor cultivation, while neither accumulation of side-products nor loss of indole were observed. To establish an efficient and robust production process, new tryptophanases were recruited by mining of bacterial sequence databases. This search retrieved more than 400 candidates and, upon screening of tryptophanase activity, nine new enzymes were identified as most promising. The highest production of indole in vivo in C. glutamicum was achieved based on the tryptophanase from Providencia rettgeri. Evaluation of several biological aspects identified the product toxicity as major bottleneck of this conversion. In situ product recovery was applied to sequester indole in a food-grade organic phase during the fermentation to avoid inhibition due to product accumulation. This process enabled complete conversion of l-tryptophan and an indole product titer of 5.7 g L⁻¹ was reached. Indole partitioned to the organic phase which contained 28 g L⁻¹ indole while no other products were observed indicating high indole purity.

Conclusions

The bioconversion production process established in this study provides an attractive route for sustainable indole production from tryptophan in C. glutamicum. Industrially relevant indole titers were achieved within 24 h and indole was concentrated in the organic layer as a pure product after the fermentation.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12934-022-01771-y.

CRISPR-assisted rational flux-tuning and arrayed CRISPRi screening of an L-proline exporter for L-proline hyperproduction

Development of hyperproducing strains is important for biomanufacturing of biochemicals and biofuels but requires extensive efforts to engineer cellular metabolism and discover functional components. Herein, we optimize and use the CRISPR-assisted editing and CRISPRi screening methods to convert a wild-type Corynebacterium glutamicum to a hyperproducer of L-proline, an amino acid with medicine, feed, and food applications. To facilitate L-proline production, feedback-deregulated variants of key biosynthetic enzyme γ-glutamyl kinase are screened using CRISPR-assisted single-stranded DNA recombineering. To increase the carbon flux towards L-proline biosynthesis, flux-control genes predicted by in silico analysis are fine-tuned using tailored promoter libraries. Finally, an arrayed CRISPRi library targeting all 397 transporters is constructed to discover an L-proline exporter Cgl2622. The final plasmid-, antibiotic-, and inducer-free strain produces L-proline at the level of 142.4 g/L, 2.90 g/L/h, and 0.31 g/g. The CRISPR-assisted strain development strategy can be used for engineering industrial-strength strains for efficient biomanufacturing.

Synthesis of C2-C4 diols from bioresources: Pathways and metabolic intervention strategies

Diols are important platform chemicals with extensive industrial applications in biopolymer synthesis, cosmetics, and fuels. The increased dependence on non-renewable sources to meet the energy requirement of the population raised issues regarding fossil fuel depletion and environmental impacts. The utilization of biological methods for the synthesis of diols by utilizing renewable resources such as glycerol and agro-residual wastes gained attention worldwide because of its advantages. Among these, biotransformation of 1,3-propanediol (1,3-PDO) and 2,3-butanediol (2,3-BDO) were extensively studied and at present, these diols are produced commercially in large scale with high yield. Many important isomers of C2-C4 diols lack natural synthetic pathways and development of chassis strains for the synthesis can be accomplished by adopting synthetic biology approaches. This current review depicts an overall idea about the pathways involved in C2-C4 diol production, metabolic intervention strategies and technologies in recent years.

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.

MarR-type transcription factor RosR regulates glutamate metabolism network and promotes accumulation of L-glutamate in Corynebacterium glutamicum G01

Transcription factors (TFs) perform a crucial function in the regulation of amino acids biosynthesis. Here, TFs involved in L-glutamate biosynthesis in Corynebacterium glutamicum were investigated. Compared to transcriptomic results of C. glutamicum 13032, 7 TFs regulated to glutamate biosynthesis were indentifed in G01 and E01. Among them, RosR was demonstrated to regulate L-glutamate metabolic network by binding to the promoters of glnA, pqo, ilvB, ilvN, ilvC, ldhA, odhA, dstr1, fas, argJ, ak and pta. Overexpression of RosR in G01 resulted in significantly decreased by-products yield and improved L-glutamate titer (130.6 g/L) and yield (0.541 g/g from glucose) in fed-batch fermentation. This study demonstrated the L-glutamate production improved by the expression of TFs in C. glutamicum, which provided a good reference for the transcriptional regulation engineering of strains for amino acid biosynthesis and suggested further metabolic engineering of C. glutamicum for L-glutamate production.

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.

GPRuler: Metabolic gene-protein-reaction rules automatic reconstruction

Metabolic network models are increasingly being used in health care and industry. As a consequence, many tools have been released to automate their reconstruction process de novo. In order to enable gene deletion simulations and integration of gene expression data, these networks must include gene-protein-reaction (GPR) rules, which describe with a Boolean logic relationships between the gene products (e.g., enzyme isoforms or subunits) associated with the catalysis of a given reaction. Nevertheless, the reconstruction of GPRs still remains a largely manual and time consuming process. Aiming at fully automating the reconstruction process of GPRs for any organism, we propose the open-source python-based framework GPRuler. By mining text and data from 9 different biological databases, GPRuler can reconstruct GPRs starting either from just the name of the target organism or from an existing metabolic model. The performance of the developed tool is evaluated at small-scale level for a manually curated metabolic model, and at genome-scale level for three metabolic models related to Homo sapiens and Saccharomyces cerevisiae organisms. By exploiting these models as benchmarks, the proposed tool shown its ability to reproduce the original GPR rules with a high level of accuracy. In all the tested scenarios, after a manual investigation of the mismatches between the rules proposed by GPRuler and the original ones, the proposed approach revealed to be in many cases more accurate than the original models. By complementing existing tools for metabolic network reconstruction with the possibility to reconstruct GPRs quickly and with a few resources, GPRuler paves the way to the study of context-specific metabolic networks, representing the active portion of the complete network in given conditions, for organisms of industrial or biomedical interest that have not been characterized metabolically yet.

Production of l-glutamate family amino acids in Corynebacterium glutamicum: Physiological mechanism, genetic modulation, and prospects

l-glutamate family amino acids (GFAAs), consisting of l-glutamate, l-arginine, l-citrulline, l-ornithine, l-proline, l-hydroxyproline, γ-aminobutyric acid, and 5-aminolevulinic acid, are widely applied in the food, pharmaceutical, cosmetic, and animal feed industries, accounting for billions of dollars of market activity. These GFAAs have many functions, including being protein constituents, maintaining the urea cycle, and providing precursors for the biosynthesis of pharmaceuticals. Currently, the production of GFAAs mainly depends on microbial fermentation using Corynebacterium glutamicum (including its related subspecies Corynebacterium crenatum), which is substantially engineered through multistep metabolic engineering strategies. This review systematically summarizes recent advances in the metabolic pathways, regulatory mechanisms, and metabolic engineering strategies for GFAA accumulation in C. glutamicum and C. crenatum, which provides insights into the recent progress in l-glutamate-derived chemical production.

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.

Knowledge extraction from literature and enzyme sequences complements FBA analysis in metabolic engineering

Flux balance analysis (FBA) using genome-scale metabolic model (GSM) is a useful method for improving the bio-production of useful compounds. However, FBA often does not impose important constraints such as nutrients uptakes, by-products excretions and gases (oxygen and carbon dioxide) transfers. Furthermore, important information on metabolic engineering such as enzyme amounts, activities, and characteristics caused by gene expression and enzyme sequences is basically not included in GSM. Therefore, simple FBA is often not sufficient to search for metabolic manipulation strategies that are useful for improving the production of target compounds. In this study, we proposed a method using literature and enzyme search to complement the FBA-based metabolic manipulation strategies. As a case study, this method was applied to shikimic acid production by Corynebacterium glutamicum to verify its usefulness. As unique strategies in literature-mining, overexpression of the transcriptional regulator SugR and gene disruption related to by-products productions were complemented. In the search for alternative enzyme sequences, it was suggested that those candidates are searched for from various species based on features captured by deep learning, which are not simply homologous to amino acid sequences of the base enzymes.

Design and construction of novel biocatalyst for bioprocessing: Recent advances and future outlook

Bioprocess, a biocatalysis-based technology, is becoming popular in many research fields and widely applied in industrial manufacturing. However, low bioconversion, low productivity, and high costs during industrial processes are usually the limitation in bioprocess. Therefore, many biocatalyst strategies have been developed to meet these challenges in recent years. In this review, we firstly discuss protein engineering strategies, which are emerged for improving the biocatalysis activity of biocatalysts. Then, we summarize metabolic engineering strategies that are promoting the development of microbial cell factories. Next, we illustrate the necessity of using the combining strategy of protein engineering and metabolic engineering for efficient biocatalysts. Lastly, future perspectives about the development and application of novel biocatalyst strategies are discussed. This review provides theoretical guidance for the development of efficient, sustainable, and economical bioprocesses mediated by novel biocatalysts.

Corynebacterium lizhenjunii sp. nov., isolated from the respiratory tract of Marmota himalayana, and Corynebacterium qintianiae sp. nov., isolated from the lung tissue of Pseudois nayaur

Four Gram-stain-positive, non-motile and asporous bacilli (strains ZJ-599^T, ZJ-621, MC1420^T and MC1482), isolated from animal tissue and environmental samples collected on the Qinghai-Tibet Plateau, PR China, were taxonomically characterized. Based on the results of 16S rRNA gene sequence analyses, the closest relatives of strains ZJ-599^T and ZJ-621 were Corynebacterium endometrii LMM-1653^T (97.5 %), Corynebacterium phocae M408/89/1^T (96.5 %) and Corynebacterium flavescens OJ8^T (96.3 %), whereas strains MC1420^T and MC1482 were closest to Corynebacterium sanguinis CCUG 58655^T (98.9 %), Corynebacterium mycetoides DSM 20632^T (98.4 %) and Corynebacterium lipophiloflavum DSM 44291^T (97.9 %). The results of rpoB gene sequence similarity analysis indicated that C. phocae M408/89/1^T and C. sanguinis CCUG 58655^T were closest to strains ZJ-599^T/ZJ-621 (83.5 %) and MC1420^T/MC1482 (91.8 %), respectively. The two novel type strains shared a similarity of 95.2 % in 16S rRNA and 81.3 % in rpoB gene sequences. The TAP-PCR DNA fingerprint and MALDI-TOF MS spectrum patterns clearly differentiated the novel isolates within and between each pair of strains. Strain ZJ-599^T had 21.9-22.4 % digital DNA-DNA hybridization (dDDH) scores with C. endometrii LMM-1653^T, C. phocae M408/89/1^T and C. flavescens OJ8^T, and 72.3-72.9 % of average nucleotide identity (ANI) with them. Similarly, strain MC1420^T had 22.9-23.7 % dDDH values with C. sanguinis CCUG 58655^T, C. mycetoides DSM 20632^T and C. lipophiloflavum DSM 44291^T, and 80.4-81.3 % ANI scores with them. Strain ZJ-599^T had a 23.1 % dDDH value and 70.5 % ANI score with strain MC1420^T, both below the corresponding thresholds for species delineation. Strains ZJ-599^T and MC1420^T both contain mycolic acids and have MK-8(H₂) and MK-9(H₂) as the predominant respiratory quinones, meso-diaminopimelic acid as the diagnostic diamino acid, and C_18 : 1  ω9c as the main fatty acid. C_17 : 1  ω8c and C_15 : 1  ω8c were predominant in strain ZJ-599^T in contrast to C_17 : 1  ω7c being predominant in strain MC1420^T. The main polar lipids in strain ZJ-599^T were diphosphatidylglycerol, phosphatidylinositol and one unidentified glycolipid, while strain MC1420^T had diphosphatidylglycerol, phosphatidylglycerol and one unidentified lipid as the major components. Since the two pairs of novel strains (ZJ-599^T/ZJ-621, MC1420^T/MC1482) distinctly differ from each other and from their nearest relatives, two novel species of the genus Corynebacterium are proposed, namely Corynebacterium lizhenjunii (type strain ZJ-599^T=GDMCC 1.1779^T=JCM 34341^T) and Corynebacterium qintianiae (type strain MC1420^T=GDMCC 1.1783^T=JCM 34340^T), respectively.

Metabolic engineering strategy for synthetizing trans-4-hydroxy-L-proline in microorganisms

Trans-4-hydroxy-L-proline is an important amino acid that is widely used in medicinal and industrial applications, particularly as a valuable chiral building block for the organic synthesis of pharmaceuticals. Traditionally, trans-4-hydroxy-L-proline is produced by the acidic hydrolysis of collagen, but this process has serious drawbacks, such as low productivity, a complex process and heavy environmental pollution. Presently, trans-4-hydroxy-L-proline is mainly produced via fermentative production by microorganisms. Some recently published advances in metabolic engineering have been used to effectively construct microbial cell factories that have improved the trans-4-hydroxy-L-proline biosynthetic pathway. To probe the potential of microorganisms for trans-4-hydroxy-L-proline production, new strategies and tools must be proposed. In this review, we provide a comprehensive understanding of trans-4-hydroxy-L-proline, including its biosynthetic pathway, proline hydroxylases and production by metabolic engineering, with a focus on improving its production.

De Novo Engineering of Corynebacterium glutamicum for l-Proline Production

l-Proline is an important amino acid that has various industrial applications. Industrial l-proline-producing strains are obtained by the mutagenesis of Corynebacterium glutamicum. In this study, the optimized C. glutamicum genome-editing tools were further applied in the de novo construction of a hyper-l-proline-producing strain. Overexpression of a feedback inhibition-resistant γ-glutamic kinase mutant ProBG149K, deletion of a proline dehydrogenase to block l-proline degradation, overexpression of glutamate dehydrogenase to increase glutamate synthesis flux, the mutation of 6-phosphate gluconate dehydrogenase and glucose-6-phosphate-dehydrogenase in the pentose phosphate pathway to enhance NADPH supply, the deletion of pyruvate aminotransferase to decrease the byproduct l-alanine synthesis, and weakening of α-ketoglutarate dehydrogenase to regulate the TCA cycle were combined to obtain ZQJY-9. ZQJY-9 produced 19.68 ± 0.22 g/L of l-proline in flask fermentation and was also demonstrated at the 3 L bioreactor level by fed-batch fermentation producing 120.18 g/L of l-proline at 76 h with the highest productivity of 1.581 g/L/h.

Directed Evolution of Ornithine Cyclodeaminase Using an EvolvR-Based Growth-Coupling Strategy for Efficient Biosynthesis of l-Proline

l-Proline takes a significant role in the pharmaceutical and chemical industries as well as graziery. Typical biosynthesis of l-proline is from l-glutamate, involving three enzyme reactions as well as a spontaneous cyclization. Alternatively, l-proline can be also synthesized in l-ornithine and/or l-arginine producing strains by an ornithine aminotransferase (OCD). In this study, a strategy of directed evolution combining rare codon selection and pEvolvR was developed to screen OCD with high catalytic efficiency, improving l-proline production from l-arginine chassis cells. The mutations were generated by CRISPR-assisted DNA polymerases and were screened by growth-coupled rare codon selection system. OCD_{K205G/M86K/T162A} from Pseudomonas putida was identified with 2.85-fold increase in catalytic efficiency for the synthesis of l-proline. Furthermore, we designed and optimized RBS for the BaargI and Ppocd coupling cascade using RedLibs, as well as sRNA inhibition of argF to moderate l-proline biosynthesis in l-arginine overproducing Corynebacterium crenatum. The strain PS6 with best performance reached 15.3 g/L l-proline in the shake flask and showed a titer of 38.4 g/L in a 5 L fermenter with relatively low concentration of residual l-ornithine and/or l-arginine.

Genome-scale modeling for Bacillus coagulans to understand the metabolic characteristics

Lactic acid is widely used in many industries, especially in the production of poly-lactic acid. Bacillus coagulans is a promising lactic acid producer in industrial fermentation due to its thermophilic property. In this study, we developed the first genome-scale metabolic model (GEM) of B. coagulans iBag597, together with an enzyme-constrained model ec-iBag597. We measured strain-specific biomass composition and integrated the data into a biomass equation. Then, we validated iBag597 against experimental data generated in this study, including amino acid requirements and carbon source utilization, showing that simulations were generally consistent with the experimental results. Subsequently, we carried out chemostats to investigate the effects of specific growth rate and culture pH on metabolism of B. coagulans. Meanwhile, we used iBag597 to estimate the intracellular metabolic fluxes for those conditions. The results showed that B. coagulans was capable of generating ATP via multiple pathways, and switched among them in response to various conditions. With ec-iBag597, we estimated the protein cost and protein efficiency for each ATP-producing pathway to investigate the switches. Our models pave the way for systems biology of B. coagulans, and our findings suggest that maintaining a proper growth rate and selecting an optimal pH are beneficial for lactate fermentation.

Current Status and Applications of Adaptive Laboratory Evolution in Industrial Microorganisms

Adaptive laboratory evolution (ALE) is an evolutionary engineering approach in artificial conditions that improves organisms through the imitation of natural evolution. Due to the development of multi-level omics technologies in recent decades, ALE can be performed for various purposes at the laboratory level. This review delineates the basics of the experimental design of ALE based on several ALE studies of industrial microbial strains and updates current strategies combined with progressed metabolic engineering, in silico modeling and automation to maximize the evolution efficiency. Moreover, the review sheds light on the applicability of ALE as a strain development approach that complies with non-recombinant preferences in various food industries. Overall, recent progress in the utilization of ALE for strain development leading to successful industrialization is discussed.

Adaptive laboratory evolution enhances methanol tolerance and conversion in engineered Corynebacterium glutamicum

Synthetic methylotrophy has recently been intensively studied to achieve methanol-based biomanufacturing of fuels and chemicals. However, attempts to engineer platform microorganisms to utilize methanol mainly focus on enzyme and pathway engineering. Herein, we enhanced methanol bioconversion of synthetic methylotrophs by improving cellular tolerance to methanol. A previously engineered methanol-dependent Corynebacterium glutamicum is subjected to adaptive laboratory evolution with elevated methanol content. Unexpectedly, the evolved strain not only tolerates higher concentrations of methanol but also shows improved growth and methanol utilization. Transcriptome analysis suggests increased methanol concentrations rebalance methylotrophic metabolism by down-regulating glycolysis and up-regulating amino acid biosynthesis, oxidative phosphorylation, ribosome biosynthesis, and parts of TCA cycle. Mutations in the O-acetyl-l-homoserine sulfhydrylase Cgl0653 catalyzing formation of l-methionine analog from methanol and methanol-induced membrane-bound transporter Cgl0833 are proven crucial for methanol tolerance. This study demonstrates the importance of tolerance engineering in developing superior synthetic methylotrophs.

Wang et al. improve the methanol tolerance for the synthetic methylotroph, Corynebacterium glutamicum. They generate 3 new strains by directed evolution and use biochemical, transcriptomic, and genetic approaches to characterize the pathways underlying the enhanced methanol metabolism. Their findings are important for biomanufacturing purposes.

Significantly enhancing production of trans-4-hydroxy-l-proline by integrated system engineering in Escherichia coli

Trans-4-hydroxy-l-proline is produced by trans-proline-4-hydroxylase with l-proline through glucose fermentation. Here, we designed a thorough "from A to Z" strategy to significantly improve trans-4-hydroxy-l-proline production. Through rare codon selected evolution, Escherichia coli M1 produced 18.2 g L-1 l-proline. Metabolically engineered M6 with the deletion of putA, proP, putP, and aceA, and proB mutation focused carbon flux to l-proline and released its feedback inhibition. It produced 15.7 g L-1 trans-4-hydroxy-l-proline with 10 g L-1 l-proline retained. Furthermore, a tunable circuit based on quorum sensing attenuated l-proline hydroxylation flux, resulting in 43.2 g L-1 trans-4-hydroxy-l-proline with 4.3 g L-1 l-proline retained. Finally, rationally designed l-proline hydroxylase gave 54.8 g L-1 trans-4-hydroxy-l-proline in 60 hours almost without l-proline remaining-the highest production to date. The de novo engineering carbon flux through rare codon selected evolution, dynamic precursor modulation, and metabolic engineering provides a good technological platform for efficient hydroxyl amino acid synthesis.