6-Phosphogluconolactonase is critical for the efficient functioning of the pentose phosphate pathway.

The metabolic networks of microorganisms are remarkably robust to genetic and environmental perturbations. This robustness stems from redundancies such as gene duplications, isoenzymes, alternative metabolic pathways, and also from non-enzymatic reactions. In the oxidative branch of the pentose phosphate pathway (oxPPP), 6-phosphogluconolactone hydrolysis into 6-phosphogluconate is catalysed by 6-phosphogluconolactonase (Pgl) but in the absence of the latter, the oxPPP flux is thought to be maintained by spontaneous hydrolysis. However, in Δpgl Escherichia coli, an extracellular pathway can also contribute to pentose phosphate synthesis. This raises question as to whether the intracellular non-enzymatic reaction can compensate for the absence of 6-phosphogluconolactonase and, ultimately, on the role of 6-phosphogluconolactonase in central metabolism. Our results validate that the bypass pathway is active in the absence of Pgl, specifically involving the extracellular spontaneous hydrolysis of gluconolactones to gluconate. Under these conditions, metabolic flux analysis reveals that this bypass pathway accounts for the entire flux into the oxPPP. This alternative metabolic route-partially extracellular-sustains the flux through the oxPPP necessary for cell growth, albeit at a reduced rate in the absence of Pgl. Importantly, these findings imply that intracellular non-enzymatic hydrolysis of 6-phosphogluconolactone does not compensate for the absence of Pgl. This underscores the crucial role of Pgl in ensuring the efficient functioning of the oxPPP.

Metabolomics reveals changes in soil metabolic profiles during vegetation succession in karst area.

Soil metabolites are critical in regulating the dynamics of ecosystem structure and function, particularly in fragile karst ecosystems. Clarification of response of soil metabolism to vegetation succession in karst areas will contribute to the overall understanding and management of karst soils. Here, we investigated the metabolite characteristics of karst soils with different vegetation stages (grassland, brushwood, secondary forest and primary forest) based on untargeted metabolomics. We confirmed that the abundance and composition of soil metabolites altered with vegetation succession. Of the 403 metabolites we found, 157 had significantly varied expression levels across vegetation soils, including mainly lipids and lipid-like molecules, phenylpropanoids and polyketides, organic acids and derivatives. Certain soil metabolites, such as maltotetraose and bifurcose, were sensitive to vegetation succession, increasing significantly from grassland to brushwood and then decreasing dramatically in secondary and primary forests, making them possible indicators of karst vegetation succession. In addition, soil metabolic pathways, such as galactose metabolism and biosynthesis of unsaturated fatty acids, also changed with vegetation succession. This study characterized the soil metabolic profile in different vegetation stages during karst secondary succession, which would provide new insights for the management of karst soils.

Metabolomics analysis of rice fermented by medicinal fungi providing insights into the preparation of functional food.

Rice, a primary staple food, may be improved in value via fermentation. Here, ten medicinal basidiomycetous fungi were separately applied for rice fermentation. After preliminary screening, Ganoderma boninense, Phylloporia pulla, Sanghuangporus sanghuang and Sanghuangporus weigelae were selected for further LC-MS based determination of the changes in metabolic profile after their fermentation with rice, and a total of 261, 296, 312, and 355 differential compounds were identified, respectively. Most of these compounds were up-regulated and involved in the metabolic pathways of amino acid metabolism, lipid metabolism, carbohydrate metabolism and the biosynthesis of other secondary metabolites. Sanghuangporus weigelae endowed the rice with the highest nutritional and bioactive values. The metabolic network of the identified differential compounds in rice fermented by S. weigelae illustrated their close relationships. In summary, this study provides insights into the preparation and application of potential functional food via the fermentation of rice with medicinal fungi.

Genome-scale metabolic network of human carotid plaque reveals the pivotal role of glutamine/glutamate metabolism in macrophage modulating plaque inflammation and vulnerability.

BACKGROUND: Metabolism is increasingly recognized as a key regulator of the function and phenotype of the primary cellular constituents of the atherosclerotic vascular wall, including endothelial cells, smooth muscle cells, and inflammatory cells. However, a comprehensive analysis of metabolic changes associated with the transition of plaque from a stable to a hemorrhaged phenotype is lacking. METHODS: In this study, we integrated two large mRNA expression and protein abundance datasets (BIKE, n = 126; MaasHPS, n = 43) from human atherosclerotic carotid artery plaque to reconstruct a genome-scale metabolic network (GEM). Next, the GEM findings were linked to metabolomics data from MaasHPS, providing a comprehensive overview of metabolic changes in human plaque. RESULTS: Our study identified significant changes in lipid, cholesterol, and inositol metabolism, along with altered lysosomal lytic activity and increased inflammatory activity, in unstable plaques with intraplaque hemorrhage (IPH+) compared to non-hemorrhaged (IPH-) plaques. Moreover, topological analysis of this network model revealed that the conversion of glutamine to glutamate and their flux between the cytoplasm and mitochondria were notably compromised in hemorrhaged plaques, with a significant reduction in overall glutamate levels in IPH+ plaques. Additionally, reduced glutamate availability was associated with an increased presence of macrophages and a pro-inflammatory phenotype in IPH+ plaques, suggesting an inflammation-prone microenvironment. CONCLUSIONS: This study is the first to establish a robust and comprehensive GEM for atherosclerotic plaque, providing a valuable resource for understanding plaque metabolism. The utility of this GEM was illustrated by its ability to reliably predict dysregulation in the cholesterol hydroxylation, inositol metabolism, and the glutamine/glutamate pathway in rupture-prone hemorrhaged plaques, a finding that may pave the way to new diagnostic or therapeutic measures.

Multi-Omics Analyses of Lettuce (Lactuca sativa) Reveals Primary Metabolism Reorganization Supporting Distinct Features of Secondary Metabolism Induced by Supplementing UV-A Radiation.

UV can serve as an effective light spectrum for regulating plant secondary metabolites, while relevant studies on UV-A are much less extensive than those on UV-B. A comprehensive understanding of the selective effects of UV-A on different secondary metabolites and the specific features of primary metabolism that drive these effects is still lacking. To address this knowledge gap, we conducted a study to analyze the dynamic changes in the metabolome and transcriptome of lettuce leaves irradiated with red plus UV-A light (monochromatic red light as control). Generally, UV-A promoted the synthesis of most phenylpropanoids and terpenoids originating from the shikimate and methylerythritol phosphate (MEP) pathway in plastids but sacrificed the synthesis of terpenoids derived from the mevalonate (MVA) pathway, particularly sesquiterpenes. Increased precursors supply for the shikimate and MEP pathway under UV-A was directly supported by the activation of the Calvin-Benson cycle and phosphoenolpyruvate transport. Whereas, along with phosphoenolpyruvate transport, the TCA cycle was restrained, causing deprivation of the MVA pathway precursor. In addition, UV-A also activated the plastidic oxidative branch of the pentose phosphate pathway, photorespiration, and malate shuttle, to ensure a sufficient supply of nitrogen, circulation homeostasis of the Calvin-Benson cycle, and energy balance, thus indirectly supporting UV-A-induced specific secondary metabolic output. This study provides a comprehensive framework for understanding the flexible primary-secondary metabolism interactions that are able to produce specific metabolites favorable for adaptation to environmental stimuli.

Large-scale, comprehensive plasma metabolomic analyses reveal potential biomarkers for the diagnosis of early-stage coronary atherosclerosis.

BACKGROUND: Coronary atherosclerosis (CAS) is a prevalent and chronic life-threatening disease. However, the detection of CAS at an early stage is difficult because of the lack of effective noninvasive diagnostic methods. The present study aimed to characterize the plasma metabolome of early-stage CAS patients to discover metabolomic biomarkers, develop a novel metabolite-based model for accurate noninvasive diagnosis of early-stage CAS, and explore the underlying metabolic mechanisms involved. METHODS: A total of 100 patients with early-stage CAS and 120 age- and sex-matched control subjects were recruited from the Chinese Han population and further randomly divided into training (n = 120) and test sets (n = 100). The metabolomic profiles of the plasma samples were analyzed by an integrated untargeted liquid chromatography-mass spectrometry approach, including two separation modes and two ionization modes. Univariate and multivariate statistical analyses were employed to identify potential biomarkers and construct an early-stage CAS diagnostic model. RESULTS: The integrated analytical method established herein improved metabolite coverage compared with single chromatographic separation and MS ionization mode. A total of 80 metabolites were identified as potential biomarkers of early-stage CAS, and these metabolites were mainly involved in glycerophospholipid, fatty acid, sphingolipid, and amino acid metabolism. An effective diagnostic model for early-stage CAS was established, incorporating 11 metabolites and achieving areas under the receiver operating characteristic curve (AUCs) of 0.984 and 0.908 in the training and test sets, respectively. CONCLUSIONS: Our study not only successfully developed an effective noninvasive diagnostic model for identifying early-stage CAS but also provided novel insights into the pathogenesis of CAS.

A metabolomics perspective on clorobiocin biosynthesis: discovery of bromobiocin and novel derivatives through LC-MSE-based molecular networking.

Clorobiocin is a well-known, highly effective inhibitor of DNA gyrase belonging to the aminocoumarin antibiotics. To identify potentially novel derivatives of this natural product, we conducted an untargeted investigation of clorobiocin biosynthesis in the known producer Streptomyces roseochromogenes DS 12.976 using LC-MSE, molecular networking, and analysis of fragmentation spectra. Previously undescribed clorobiocin derivatives uncovered in this study include bromobiocin, a variant halogenated with bromine instead of chlorine, hydroxylated clorobiocin, carrying an additional hydroxyl group on its 5-methyl-pyrrole 2-carboxyl moiety, and two other derivatives with modifications on their 3-dimethylallyl 4-hydroxybenzoate moieties. Furthermore, we identified several compounds not previously considered clorobiocin pathway products, which provide new insights into the clorobiocin biosynthetic pathway. By supplementing the medium with different concentrations of potassium bromide, we confirmed that the clorobiocin halogenase can utilize bromine instead of chlorine. The reaction, however, is impeded such that non-halogenated clorobiocin derivatives accumulate. Preliminary assays indicate that the antibacterial activity of bromobioin against Bacillus subtilis and efflux-impaired Escherichia coli matches that of clorobiocin. Our findings emphasize that yet unexplored compounds can be discovered from established strains and biosynthetic gene clusters by means of metabolomics analysis and highlight the utility of LC-MSE-based methods to contribute to unraveling natural product biosynthetic pathways. IMPORTANCE: The aminocoumarin clorobiocin is a well-known gyrase inhibitor produced by the gram-positive bacterium Streptomyces roseochromogenes DS 12.976. To gain a deeper understanding of the biosynthetic pathway of this complex composite of three chemically distinct entities and the product spectrum, we chose a metabolite-centric approach. Employing high-resolution LC-MSE analysis, we investigated the pathway products in extracted culture supernatants of the natural producer. Novel pathway products were identified that expand our understanding of three aspects of the biosynthetic pathway, namely the modification of the noviose, transfer and methylation of the pyrrole 2-carboxyl moiety, and halogenation. For the first time, brominated products were detected. Their levels and the levels of non-halogenated products increased in medium supplemented with KBr. Based on the presented data, we propose that the enzyme promiscuity contributes to a broad product spectrum.

Cordyceps militaris: A novel mushroom platform for metabolic engineering.

Cordyceps militaris, widely recognized as a medicinal and edible mushroom in East Asia, contains a variety of bioactive compounds, including cordycepin (COR), pentostatin (PTN) and other high-value compounds. This review explores the potential of developing C. militaris as a cell factory for the production of high-value chemicals and nutrients. This review comprehensively summarizes the fermentation advantages, metabolic networks, expression elements, and genome editing tools specific to C. militaris and discusses the challenges and barriers to further research on C. militaris across various fields, including computational biology, existing DNA elements, and genome editing approaches. This review aims to describe specific and promising opportunities for the in-depth study and development of C. militaris as a new chassis cell. Additionally, to increase the practicability of this review, examples of the construction of cell factories are provided, and promising strategies for synthetic biology development are illustrated.

Integrative metagenomics, volatilomics and chemometrics for deciphering the microbial structure and core metabolic network during Chinese rice wine (Huangjiu) fermentation in different regions.

Huangjiu is a spontaneously fermented alcoholic beverage, that undergoes intricate microbial compositional changes. This study aimed to unravel the flavor and quality formation mechanisms based on the microbial metabolism of Huangjiu. Here, metagenome techniques, chemometrics analysis, and headspace solid-phase microextraction gas chromatography-mass spectrometry (HS-SPME-GC-MS) metabolomics combined with microbial metabolic network were employed to investigate the distinctions and relationship between the microbial profiles and the quality characteristics, flavor metabolites, functional metabolic patterns of Huangjiu across three regions. Significant variations (P < 0.05) were observed in metabolic rate of physicochemical parameters and biogenic amine concentration among three regions. 8 aroma compounds (phenethyl acetate, phenylethyl alcohol, isobutyl alcohol, ethyl octanoate, ethyl acetate, ethyl hexanoate, isoamyl alcohol, and diethyl succinate) out of 448 volatile compounds were identified as the regional chemical markers. 25 dominant microbial genera were observed through metagenomic analysis, and 13 species were confirmed as microbial markers in three regions. A metabolic network analysis revealed that Saccharomycetales (Saccharomyces), Lactobacillales (Lactobacillus, Weissella, and Leuconostoc), and Eurotiales (Aspergillus) were the predominant populations responsible for substrate, flavor (mainly esters and phenylethyl alcohol) metabolism, Lactobacillales and Enterobacterales were closely linked with biogenic amine. These findings provide scientific evidence for regional microbial contributions to geographical characteristics of Huangjiu, and perspectives for optimizing microbial function to promote Huangjiu quality.

Harnessing inter-kingdom metabolic disparities at the human-fungal interface for novel therapeutic approaches.

Humans interact with a multitude of microorganisms in various ecological relationships, ranging from commensalism to pathogenicity. The same applies to fungi, long recognized for their pathogenic roles in infection-such as in invasive fungal diseases caused, among others, by Aspergillus fumigatus and Candida spp.-and, more recently, for their beneficial activities as an integral part of the microbiota. Indeed, alterations in the fungal component of the microbiota, or mycobiota, have been associated with inflammatory, infectious and metabolic diseases, and cancer. Whether acting as opportunistic pathogens or symbiotic commensals, fungi possess a complex enzymatic repertoire that intertwines with that of the host. In this metabolic cross-talk, fungal enzymes may be unique, thus providing novel metabolic opportunities to the host, or, conversely, produce toxic metabolites. Indeed, administration of fungal probiotics and fungi-derived products may be beneficial in inflammatory and infectious diseases, but fungi may also produce a plethora of toxic secondary metabolites, collectively known as mycotoxins. Fungal enzymes may also be homologues to human enzymes, but nevertheless embedded in fungal-specific metabolic networks, determined by all the interconnected enzymes and molecules, quantitatively and qualitatively specific to the network, such that the activity and metabolic effects of each enzyme remain unique to fungi. In this Opinion, we explore the concept that targeting this fungal metabolic unicity, either in opportunistic pathogens or commensals, may be exploited to develop novel therapeutic strategies. In doing so, we present our recent experience in different pathological settings that ultimately converge on relevant trans-kingdom metabolic differences.

A global view of T cell metabolism in systemic lupus erythematosus.

Impaired metabolism is recognized as an important contributor to pathogenicity of T cells in Systemic Lupus Erythematosus (SLE). Over the last two decades, we have acquired significant knowledge about the signaling and transcriptomic programs related to metabolic rewiring in healthy and SLE T cells. However, our understanding of metabolic network activity derives largely from studying metabolic pathways in isolation. Here, we argue that enzymatic activities are necessarily coupled through mass and energy balance constraints with in-built network-wide dependencies and compensation mechanisms. Therefore, metabolic rewiring of T cells in SLE must be understood in the context of the entire network, including changes in metabolic demands such as shifts in biomass composition and cytokine secretion rates as well as changes in uptake/excretion rates of multiple nutrients and waste products. As a way forward, we suggest cell physiology experiments and integration of orthogonal metabolic measurements through computational modeling towards a comprehensive understanding of T cell metabolism in lupus.

Self-generating autocatalytic networks: structural results, algorithms and their relevance to early biochemistry.

The concept of an autocatalytic network of reactions that can form and persist, starting from just an available food source, has been formalized by the notion of a reflexively autocatalytic and food-generated (RAF) set. The theory and algorithmic results concerning RAFs have been applied to a range of settings, from metabolic questions arising at the origin of life, to ecological networks, and cognitive models in cultural evolution. In this article, we present new structural and algorithmic results concerning RAF sets, by studying more complex modes of catalysis that allow certain reactions to require multiple catalysts (or to not require catalysis at all), and discuss the differing ways catalysis has been viewed in the literature. We also focus on the structure and analysis of minimal RAFs and derive structural results and polynomial-time algorithms. We then apply these new methods to a large metabolic network to gain insights into possible biochemical scenarios near the origin of life.

Integrating Network Pharmacology, Quantitative Metabolic Network Analysis, In Vitro Experiments, and Molecular Dynamics to Explore the Mechanism of Angelica Sinensis for Regulating Bone Metabolism.

BACKGROUND: Bone metabolic diseases are serious health issues worldwide. Angelica sinensis (AS) is traditionally used in Chinese medicine for treating bone metabolism diseases clinically. However, the mechanism of AS in regulating bone metabolism remains uncertain. OBJECTIVE: The current investigation was structured to elucidate the potential mechanisms of AS for modulating bone metabolism. METHODS: Firstly, targets of AS regulating bone metabolism were collected by network pharmacology. Then, the transcriptional regulation of RUNX2 was enriched as one of the key pathways for AS to regulate bone metabolism, constructing its metabolic network. Secondly, combining molecular docking, network efficiency, and network flux analyses, we conducted a quantitative evaluation of the metabolic network to reveal the potential mechanisms and components of AS regulating bone metabolism. Finally, we explored the effect of AS on the differentiation of osteoclasts from M-CSF and RANKL-induced RAW264.7 cells, as well as its impact on the osteogenic induction of MC3T3-E1 cells. We verified the mechanism and key targets of AS on bone metabolism using qRT-PCR. Furthermore, the key component was preliminarily validated through molecular dynamics simulation. RESULTS: Quantitative metabolic network of the transcriptional regulation of RUNX2 was constructed to illustrate the potential mechanism of AS for regulating bone metabolism, indicating that ferulic acid may be a pharmacological component of AS that interferes with bone metabolism. AS suppressed osteoclast differentiation in M-CSF and RANKL-induced RAW264.7 cells and reversed the expressions of osteoclastic differentiation markers, including RUNX2 and SRC. Additionally, AS induced osteogenic generation in MC3T3-E1 cells and reversed the expressions of markers associated with osteoblastic generation, such as RUNX2 and HDAC4. Molecular dynamics simulation displayed a strong binding affinity among ferulic acid, HDAC4 and SRC. CONCLUSION: This study reveals a systematic perspective on the intervention bone mechanism of AS by transcriptive regulation by RUNX2, guiding the clinical use of AS in treating diseases of the skeletal system.

Integrating Genome-Scale Metabolic Models with Patient Plasma Metabolome to Study Endothelial Metabolism In Situ.

Patient blood samples are invaluable in clinical omics databases, yet current methodologies often fail to fully uncover the molecular mechanisms driving patient pathology. While genome-scale metabolic models (GEMs) show promise in systems medicine by integrating various omics data, having only exometabolomic data remains a limiting factor. To address this gap, we introduce a comprehensive pipeline integrating GEMs with patient plasma metabolome. This pipeline constructs case-specific GEMs using literature-based and patient-specific metabolomic data. Novel computational methods, including adaptive sampling and an in-house developed algorithm for the rational exploration of the sampled space of solutions, enhance integration accuracy while improving computational performance. Model characterization involves task analysis in combination with clustering methods to identify critical cellular functions. The new pipeline was applied to a cohort of trauma patients to investigate shock-induced endotheliopathy using patient plasma metabolome data. By analyzing endothelial cell metabolism comprehensively, the pipeline identified critical therapeutic targets and biomarkers that can potentially contribute to the development of therapeutic strategies. Our study demonstrates the efficacy of integrating patient plasma metabolome data into computational models to analyze endothelial cell metabolism in disease contexts. This approach offers a deeper understanding of metabolic dysregulations and provides insights into diseases with metabolic components and potential treatments.

Reconstruction of Metabolic-Protein Interaction Integrated Network of Eriocheir sinensis and Analysis of Ecdysone Synthesis.

Integrated networks have become a new interest in genome-scale network research due to their ability to comprehensively reflect and analyze the molecular processes in cells. Currently, none of the integrated networks have been reported for higher organisms. Eriocheir sinensis is a typical aquatic animal that grows through ecdysis. Ecdysone has been identified to be a crucial regulator of ecdysis, but the influence factors and regulatory mechanisms of ecdysone synthesis in E. sinensis are still unclear. In this work, the genome-scale metabolic network and protein-protein interaction network of E. sinensis were integrated to reconstruct a metabolic-protein interaction integrated network (MPIN). The MPIN was used to analyze the influence factors of ecdysone synthesis through flux variation analysis. In total, 236 integrated reactions (IRs) were found to influence the ecdysone synthesis of which 16 IRs had a significant impact. These IRs constitute three ecdysone synthesis routes. It is found that there might be alternative pathways to obtain cholesterol for ecdysone synthesis in E. sinensis instead of absorbing it directly from the feeds. The MPIN reconstructed in this work is the first integrated network for higher organisms. The analysis based on the MPIN supplies important information for the mechanism analysis of ecdysone synthesis in E. sinensis.

Development and applications of genome-scale metabolic network models.

The genome-scale metabolic network model is an effective tool for characterizing the gene-protein-response relationship in the entire metabolic pathway of an organism. By combining various algorithms, the genome-scale metabolic network model can effectively simulate the influence of a specific environment on the physiological state of cells, optimize the culture conditions of strains, and predict the targets of genetic modification to achieve targeted modification of strains. In this review, we summarize the whole process of model building, sort out the various tools that may be involved in the model building process, and explain the role of various algorithms in model analysis. In addition, we also summarized the application of GSMM in network characteristics, cell phenotypes, metabolic engineering, etc. Finally, we discuss the current challenges facing GSMM.

Toxic mechanism in Daphnia magna due to phthalic acid esters and CuO nanoparticles co-exposure: The insight of physiological, microbiomic and metabolomic profiles.

Various phthalic acid esters (PAEs) such as dibutyl phthalate (DBP) and butyl benzyl phthalate (BBP) co-exist with nanopollutants in aquatic environment. In this study, Daphnia magna was exposed to nano-CuO and DBP or BBP at environmental relevant concentrations for 21-days to investigate these combined toxic effects. Acute EC50 values (48 h) of nano-CuO, DBP, and BBP were 12.572 mg/L, 8.978 mg/L, and 4.785 mg/L, respectively. Results showed that co-exposure with nano-CuO (500 µg/L) for 21 days significantly enhanced the toxicity of DBP (100 µg/L) and BBP (100 µg/L) to Daphnia magna by 18.37% and 18.11%, respectively. The activities of superoxide dismutase, catalase, and glutathione S-transferase were enhanced by 10.95% and 14.07%, 25.63% and 25.91%, and 39.93% and 35.01% in nano-CuO+DBP and nano-CuO+BBP treatments as compared to the individual exposure groups, verifying that antioxidative defense responses were activated. Furthermore, the co-exposure of nano-CuO and PAEs decreased the population richness and diversity microbiota, and changed the microbial community composition in Daphnia magna. Metabolomic analysis elucidated that nano-CuO + PAEs exposure induced stronger disturbance on metabolic network and molecular function, including amino acid, nucleotides, and lipid metabolism-related metabolic pathways, as comparison to PAEs single exposure treatments. In summary, the integration of physiological, microflora, and untargeted metabolomics analysis offers a fresh perspective into the potential ecological risk associated with nanopollutants and phthalate pollution in aquatic ecosystems.

Self-controlled in silico gene knockdown strategies to enhance the sustainable production of heterologous terpenoid by Saccharomyces cerevisiae.

Microbial bioengineering is a growing field for producing plant natural products (PNPs) in recent decades, using heterologous metabolic pathways in host cells. Once heterologous metabolic pathways have been introduced into host cells, traditional metabolic engineering techniques are employed to enhance the productivity and yield of PNP biosynthetic routes, as well as to manage competing pathways. The advent of computational biology has marked the beginning of a novel epoch in strain design through in silico methods. These methods utilize genome-scale metabolic models (GEMs) and flux optimization algorithms to facilitate rational design across the entire cellular metabolic network. However, the implementation of in silico strategies can often result in an uneven distribution of metabolic fluxes due to the rigid knocking out of endogenous genes, which can impede cell growth and ultimately impact the accumulation of target products. In this study, we creatively utilized synthetic biology to refine in silico strain design for efficient PNPs production. OptKnock simulation was performed on the GEM of Saccharomyces cerevisiae OA07, an engineered strain for oleanolic acid (OA) bioproduction that has been reported previously. The simulation predicted that the single deletion of fol1, fol2, fol3, abz1, and abz2, or a combined knockout of hfd1, ald2 and ald3 could improve its OA production. Consequently, strains EK1∼EK7 were constructed and cultivated. EK3 (OA07△fol3), EK5 (OA07△abz1), and EK6 (OA07△abz2) had significantly higher OA titers in a batch cultivation compared to the original strain OA07. However, these increases were less pronounced in the fed-batch mode, indicating that gene deletion did not support sustainable OA production. To address this, we designed a negative feedback circuit regulated by malonyl-CoA, a growth-associated intermediate whose synthesis served as a bypass to OA synthesis, at fol3, abz1, abz2, and at acetyl-CoA carboxylase-encoding gene acc1, to dynamically and autonomously regulate the expression of these genes in OA07. The constructed strains R_3A, R_5A and R_6A had significantly higher OA titers than the initial strain and the responding gene-knockout mutants in either batch or fed-batch culture modes. Among them, strain R_3A stand out with the highest OA titer reported to date. Its OA titer doubled that of the initial strain in the flask-level fed-batch cultivation, and achieved at 1.23 ± 0.04 g L-1 in 96 h in the fermenter-level fed-batch mode. This indicated that the integration of optimization algorithm and synthetic biology approaches was efficiently rational for PNP-producing strain design.

Synthetic Vesicles for Sustainable Energy Recycling and Delivery of Building Blocks for Lipid Biosynthesis†.

ATP is a universal energy currency that is essential for life. l-Arginine degradation via deamination is an elegant way to generate ATP in synthetic cells, which is currently limited by a slow l-arginine/l-ornithine exchange. We are now implementing a new antiporter with better kinetics to obtain faster ATP recycling. We use l-arginine-dependent ATP formation for the continuous synthesis and export of glycerol 3-phosphate by including glycerol kinase and the glycerol 3-phosphate/Pi antiporter. Exported glycerol 3-phosphate serves as a precursor for the biosynthesis of phospholipids in a second set of vesicles, which forms the basis for the expansion of the cell membrane. We have therefore developed an out-of-equilibrium metabolic network for ATP recycling, which has been coupled to lipid synthesis. This feeder-utilizer system serves as a proof-of-principle for the systematic buildup of synthetic cells, but the vesicles can also be used to study the individual reaction networks in confinement.

Investigating the Role of ß-Disodium Glycerophosphate and Urea in Promoting Growth of Streptococcus thermophilus from Omics-Integrated Genome-Scale Models.

This study investigates the impact of urea and ß-GP on the growth of Streptococcus thermophilus S-3, a bacterium commonly used in industrial fermentation processes. Through a series of growth experiments, transcriptome, metabolome, and omics-based analyses, the research demonstrates that both urea and ß-GP can enhance the biomass of S. thermophilus, with urea showing a more significant effect. The optimal urea concentration for growth was determined to be 3 g/L in M17 medium. The study also highlights the metabolic pathways influenced by urea and ß-GP, particularly the galactose metabolism pathway, which is crucial for cell growth when lactose is the substrate. The integration of omics data into the genome-scale metabolic model of S. thermophilus, iCH502, allowed for a more accurate prediction of metabolic fluxes and growth rates. The study concludes that urea can serve as a viable substitute for ß-GP in the cultivation of S. thermophilus, offering potential cost and efficiency benefits in industrial fermentation processes. The findings are supported by validation experiments with 11 additional strains of S. thermophilus, which showed increased biomass in UM17 medium.