Quantitative prediction of cellular metabolism with constraint-based models: the COBRA Toolbox v2.0

Systems biology approach for enhancing limonene yield by re-engineering Escherichia coli

Engineered microorganisms have emerged as viable alternatives for limonene production. However, issues such as low enzyme abundance or activities, and regulatory feedback/forward inhibition may reduce yields. To understand the underlying metabolism, we adopted a systems biology approach for an engineered limonene-producing Escherichia coli strain K-12 MG1655. Firstly, we generated time-series metabolomics data and, secondly, developed a dynamic model based on enzyme dynamics to track the native metabolic networks and the engineered mevalonate pathway. After several iterations of model fitting with experimental profiles, which also included 13C-tracer studies, we performed in silico knockouts (KOs) of all enzymes to identify bottleneck(s) for optimal limonene yields. The simulations indicated that ALDH/ADH (aldehyde dehydrogenase/alcohol dehydrogenase) and LDH (lactate dehydrogenase) suppression, and HK (hexokinase) enhancement would increase limonene yields. Experimental confirmation was achieved, where ALDH-ADH and LDH KOs, and HK overexpression improved limonene yield by 8- to 11-fold. Our systems biology approach can guide microbial strain re-engineering for optimal target production.

Electrostatic ethanol fermentation: Experimental study and kinetic-based metabolic modeling

Due to the electrical nature of the cell, it is possible to modulate its behavior through the application of non-lethal external electric fields to improve fermentation processes. In this work, a microbial cell system with a chamber and two electrodes inside and connected to a voltage source was used. One of the electrodes was kept isolated to create an electric field without the flow of current. Cultures with two ethanol-producing microbial strains (Saccharomyces cerevisiae and Zymomonas mobilis) were conducted in this device. The application of voltages between 0 and 18 V was evaluated to determine the impact of the generated electric field on ethanol production. To analyze the possible effect of the field on the central carbon metabolism in each strain, biochemical-based kinetic models were formulated to describe the experimental fermentation kinetics obtained. It was found that low applied voltages did not have significant effects on growth rate in either strain, but all voltages evaluated increased substrate consumption and ethanol production rate in Z. mobilis, while only 18 V affected these rates in S. cerevisiae, indicating that Z. mobilis was the most sensitive to the electric field. At the end of the fermentation, significant increases in ethanol yields of 10.7% and 19.5% were detected for S. cerevisiae and Z. mobilis, respectively. The proposed mathematical models showed that substrate transport through the membrane catalyzed by the phosphotransferase system (PTS) for Z. mobilis and hexose transport proteins mechanism and hexokinase (HK) activity for S. cerevisiae and the transformation of pyruvate to ethanol, catalyzed by the decarboxylase (PDC) and alcohol dehydrogenase (ADH) enzymes, were the reactions most affected by the application of the external field.

Spatial chemistry of citrus reveals molecules bactericidal to Candidatus Liberibacter asiaticus

Huanglongbing (HLB), associated with the psyllid-vectored phloem-limited bacterium, Candidatus Liberibacter asiaticus (CLas), is a disease threat to all citrus production worldwide. Currently, there are no sustainable curative or prophylactic treatments available. In this study, we utilized mass spectrometry (MS)-based metabolomics in combination with 3D molecular mapping to visualize complex chemistries within plant tissues to explore how these chemistries change in vivo in HLB-infected trees. We demonstrate how spatial information from molecular maps of branches and single leaves yields insight into the biology not accessible otherwise. In particular, we found evidence that flavonoid biosynthesis is disrupted in HLB-infected trees, and an increase in the polyamine, feruloylputrescine, is highly correlated with an increase in disease severity. Based on mechanistic details revealed by these molecular maps, followed by metabolic modeling, we formulated and tested the hypothesis that CLas infection either directly or indirectly converts the precursor compound, ferulic acid, to feruloylputrescine to suppress the antimicrobial effects of ferulic acid and biosynthetically downstream flavonoids. Using in vitro bioassays, we demonstrated that ferulic acid and bioflavonoids are indeed highly bactericidal to CLas, with the activity on par with a reference antibiotic, oxytetracycline, recently approved for HLB management. We propose these compounds should be evaluated as therapeutics alternatives to the antibiotics for HLB treatment. Overall, the utilized 3D metabolic mapping approach provides a promising methodological framework to identify pathogen-specific inhibitory compounds in planta for potential prophylactic or therapeutic applications.

A hybrid RNA-protein biosensor for high-throughput screening of adenosylcobalamin biosynthesis

Genetically encoded circuits have been successfully utilized to assess and characterize target variants with desirable traits from large mutant libraries. Adenosylcobalamin is an essential coenzyme that is required in many intracellular physiological reactions and is widely used in the pharmaceutical and food industries. High-throughput screening techniques capable of detecting adenosylcobalamin productivity and selecting superior adenosylcobalamin biosynthesis strains are critical for the creation of an effective microbial cell factory for the production of adenosylcobalamin at an industrial level. In this study, we developed an RNA-protein hybrid biosensor whose input part was an endogenous RNA riboswitch to specifically respond to adenosylcobalamin, the inverter part was an orthogonal transcriptional repressor to obtain signal inversion, and the output part was a fluorescent protein to be easily detected. The hybrid biosensor could specifically and positively correlate adenosylcobalamin concentrations to green fluorescent protein expression levels in vivo. This study also improved the operating concentration and dynamic range of the hybrid biosensor by systematic optimization. An individual cell harboring the hybrid biosensor presented over 20-fold higher fluorescence intensity than the negative control. Then, using such a biosensor combined with fluorescence-activated cell sorting, we established a high-throughput screening platform for screening adenosylcobalamin overproducers. This study demonstrates that this platform has significant potential to quickly isolate high-productive strains to meet industrial demand and that the framework is acceptable for various metabolites.

Machine learning for the advancement of genome-scale metabolic modeling

Constraint-based modeling (CBM) has evolved as the core systems biology tool to map the interrelations between genotype, phenotype, and external environment. The recent advancement of high-throughput experimental approaches and multi-omics strategies has generated a plethora of new and precise information from wide-ranging biological domains. On the other hand, the continuously growing field of machine learning (ML) and its specialized branch of deep learning (DL) provide essential computational architectures for decoding complex and heterogeneous biological data. In recent years, both multi-omics and ML have assisted in the escalation of CBM. Condition-specific omics data, such as transcriptomics and proteomics, helped contextualize the model prediction while analyzing a particular phenotypic signature. At the same time, the advanced ML tools have eased the model reconstruction and analysis to increase the accuracy and prediction power. However, the development of these multi-disciplinary methodological frameworks mainly occurs independently, which limits the concatenation of biological knowledge from different domains. Hence, we have reviewed the potential of integrating multi-disciplinary tools and strategies from various fields, such as synthetic biology, CBM, omics, and ML, to explore the biochemical phenomenon beyond the conventional biological dogma. How the integrative knowledge of these intersected domains has improved bioengineering and biomedical applications has also been highlighted. We categorically explained the conventional genome-scale metabolic model (GEM) reconstruction tools and their improvement strategies through ML paradigms. Further, the crucial role of ML and DL in omics data restructuring for GEM development has also been briefly discussed. Finally, the case-study-based assessment of the state-of-the-art method for improving biomedical and metabolic engineering strategies has been elaborated. Therefore, this review demonstrates how integrating experimental and in silico strategies can help map the ever-expanding knowledge of biological systems driven by condition-specific cellular information. This multiview approach will elevate the application of ML-based CBM in the biomedical and bioengineering fields for the betterment of society and the environment.

Origin of biogeographically distinct ecotypes during laboratory evolution

Resource partitioning is central to the incredible productivity of microbial communities, including gigatons in annual methane emissions through syntrophic interactions. Previous work revealed how a sulfate reducer (Desulfovibrio vulgaris, Dv) and a methanogen (Methanococcus maripaludis, Mm) underwent evolutionary diversification in a planktonic context, improving stability, cooperativity, and productivity within 300-1000 generations. Here, we show that mutations in just 15 Dv and 7 Mm genes within a minimal assemblage of this evolved community gave rise to co-existing ecotypes that were spatially enriched within a few days of culturing in a fluidized bed reactor. The spatially segregated communities partitioned resources in the simulated subsurface environment, with greater lactate utilization by attached Dv but partial utilization of resulting H2 by low affinity hydrogenases of Mm in the same phase. The unutilized H2 was scavenged by high affinity hydrogenases of planktonic Mm, producing copious amounts of methane. Our findings show how a few mutations can drive resource partitioning amongst niche-differentiated ecotypes, whose interplay synergistically improves productivity of the entire mutualistic community.

Transport-controlled growth decoupling for self-induced protein expression with a glycerol-repressible genetic circuit

Decoupling cell formation from recombinant protein synthesis is a potent strategy to intensify bioprocesses. Escherichia coli strains with mutations in the glucose uptake components lack catabolite repression, display low growth rate, no overflow metabolism, and high recombinant protein yields. Fast growth rates were promoted by the simultaneous consumption of glucose and glycerol, and this was followed by a phase of slow growth, when only glucose remained in the medium. A glycerol-repressible genetic circuit was designed to autonomously induce recombinant protein expression. The engineered strain bearing the genetic circuit was cultured in 3.9 g L-1 glycerol + 18 g L-1 glucose in microbioreactors with online oxygen transfer rate monitoring. The growth was fast during the simultaneous consumption of both carbon sources (C-sources), while expression of the recombinant protein was low. When glycerol was depleted, the growth rate decreased, and the specific fluorescence reached values 17% higher than those obtained with a strong constitutive promoter. Despite the relatively high amount of C-source used, no oxygen limitation was observed. The proposed approach eliminates the need for the substrate feeding or inducers addition and is set as a simple batch culture while mimicking fed-batch performance.

Rewiring Saccharomyces cerevisiae metabolism for optimised Taxol® precursors production

Saccharomyces cerevisiae has been conveniently used to produce Taxol® anticancer drug early precursors. However, the harmful impact of oxidative stress by the first cytochrome P450-reductase enzymes (CYP725A4-POR) of Taxol® pathway has hampered sufficient progress in yeast. Here, we evolved an oxidative stress-resistant yeast strain with three-fold higher titre of their substrate, taxadiene. The performance of the evolved and parent strains were then evaluated in galactose-limited chemostats before and under the oxidative stress by an oxidising agent. The interaction of evolution and oxidative stress was comprehensively evaluated through transcriptomics and metabolite profiles integration in yeast enzyme-constrained genome scale model. Overall, the evolved strain showed improved respiration, reduced overflow metabolites production and oxidative stress re-induction tolerance. The cross-protection mechanism also potentially contributed to better heme, flavin and NADPH availability, essential for CYP725A4 and POR optimal activity in yeast. The results imply that the evolved strain is a robust cell factory for future efforts towards Taxol© production.

Genome-scale metabolic modeling of Thraustochytrium sp. RT2316-16: Effects of nutrients on metabolism

Marine thraustochytrids produce metabolically important lipids such as the long-chain omega-3 polyunsaturated fatty acids, carotenoids, and sterols. The growth and lipid production in thraustochytrids depends on the composition of the culture medium that often contains yeast extract as a source of amino acids. This work discusses the effects of individual amino acids provided in the culture medium as the only source of nitrogen, on the production of biomass and lipids by the thraustochytrid Thraustochytrium sp. RT2316-16. A reconstructed metabolic network based on the annotated genome of RT2316-16 in combination with flux balance analysis was used to explain the observed growth and consumption of the nutrients. The culture kinetic parameters estimated from the experimental data were used to constrain the flux via the nutrient consumption rates and the specific growth rate of the triacylglycerol-free biomass in the genome-scale metabolic model (GEM) to predict the specific rate of ATP production for cell maintenance. A relationship was identified between the specific rate of ATP production for maintenance and the specific rate of glucose consumption. The GEM and the derived relationship for the production of ATP for maintenance were used in linear optimization problems, to successfully predict the specific growth rate of RT2316-16 in different experimental conditions.

Itaconic acid production by co-feeding of Ustilago maydis: A combined approach of experimental data, design of experiments, and metabolic modeling

Itaconic acid is a platform chemical with a range of applications in polymer synthesis and is also discussed for biofuel production. While produced in industry from glucose or sucrose, co-feeding of glucose and acetate was recently discussed to increase itaconic acid production by the smut fungus Ustilago maydis. In this study, we investigate the optimal co-feeding conditions by interlocking experimental and computational methods. Flux balance analysis indicates that acetate improves the itaconic acid yield up to a share of 40% acetate on a carbon molar basis. A design of experiment results in the maximum yield of 0.14 itaconic acid per carbon source from 100 g L - 1 $\,\text{g L}{}^{-1}$ glucose and 12 g L - 1 $\,\text{g L}{}^{-1}$ acetate. The yield is improved by around 22% when compared to feeding of glucose as sole carbon source. To further improve the yield, gene deletion targets are discussed that were identified using the metabolic optimization tool OptKnock. The study contributes ideas to reduce land use for biotechnology by incorporating acetate as co-substrate, a C2-carbon source that is potentially derived from carbon dioxide.

Convex Representation of Metabolic Networks with Michaelis-Menten Kinetics

Polyhedral models of metabolic networks are computationally tractable and can predict some cellular functions. A longstanding challenge is incorporating metabolites without losing tractability. In this paper, we do so using a new second-order cone representation of the Michaelis-Menten kinetics. The resulting model consists of linear stoichiometric constraints alongside second-order cone constraints that couple the reaction fluxes to metabolite concentrations. We formulate several new problems around this model: conic flux balance analysis, which augments flux balance analysis with metabolite concentrations; dynamic conic flux balance analysis; and finding minimal cut sets of networks with both reactions and metabolites. Solving these problems yields information about both fluxes and metabolite concentrations. They are second-order cone or mixed-integer second-order cone programs, which, while not as tractable as their linear counterparts, can nonetheless be solved at practical scales using existing software.

Inverse-Vaccines for Rheumatoid Arthritis Re-establish Metabolic and Immunological Homeostasis in Joint Tissues

Rheumatoid arthritis (RA) causes immunological and metabolic imbalances in tissue, exacerbating inflammation in affected joints. Changes in immunological and metabolic tissue homeostasis at different stages of RA are not well understood. Herein, the changes in the immunological and metabolic profiles in different stages in collagen induced arthritis (CIA), namely, early, intermediate, and late stage is examined. Moreover, the efficacy of the inverse-vaccine, paKG(PFK15+bc2) microparticle, to restore tissue homeostasis at different stages is also investigated. Immunological analyses of inverse-vaccine-treated group revealed a significant decrease in the activation of pro-inflammatory immune cells and remarkable increase in regulatory T-cell populations in the intermediate and late stages compared to no treatment. Also, glycolysis in the spleen is normalized in the late stages of CIA in inverse-vaccine-treated mice, which is similar to no-disease tissues. Metabolomics analyses revealed that metabolites UDP-glucuronic acid and L-Glutathione oxidized are significantly altered between treatment groups, and thus might provide new druggable targets for RA treatment. Flux metabolic modeling identified amino acid and carnitine pathways as the central pathways affected in arthritic tissue with CIA progression. Overall, this study shows that the inverse-vaccines initiate early re-establishment of homeostasis, which persists through the disease span.

LipidSIM: Inferring mechanistic lipid biosynthesis perturbations from lipidomics with a flexible, low-parameter, Markov modeling framework

Lipid metabolism is a complex and dynamic system involving numerous enzymes at the junction of multiple metabolic pathways. Disruption of these pathways leads to systematic dyslipidemia, a hallmark of many pathological developments, such as nonalcoholic steatohepatitis and diabetes. Recent advances in computational tools can provide insights into the dysregulation of lipid biosynthesis, but limitations remain due to the complexity of lipidomic data, limited knowledge of interactions among involved enzymes, and technical challenges in standardizing across different lipid types. Here, we present a low-parameter, biologically interpretable framework named Lipid Synthesis Investigative Markov model (LipidSIM), which models and predicts the source of perturbations in lipid biosynthesis from lipidomic data. LipidSIM achieves this by accounting for the interdependency between the lipid species via the lipid biosynthesis network and generates testable hypotheses regarding changes in lipid biosynthetic reactions. This feature allows the integration of lipidomics with other omics types, such as transcriptomics, to elucidate the direct driving mechanisms of altered lipidomes due to treatments or disease progression. To demonstrate the value of LipidSIM, we first applied it to hepatic lipidomics following Keap1 knockdown and found that changes in mRNA expression of the lipid pathways were consistent with the LipidSIM-predicted fluxes. Second, we used it to study lipidomic changes following intraperitoneal injection of CCl4 to induce fast NAFLD/NASH development and the progression of fibrosis and hepatic cancer. Finally, to show the power of LipidSIM for classifying samples with dyslipidemia, we used a Dgat2-knockdown study dataset. Thus, we show that as it demands no a priori knowledge of enzyme kinetics, LipidSIM is a valuable and intuitive framework for extracting biological insights from complex lipidomic data.

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.

Adjusting for false discoveries in constraint-based differential metabolic flux analysis

One of the critical steps to characterize metabolic alterations in multifactorial diseases, as well as their heterogeneity across different patients, is the identification of reactions that exhibit significantly different usage (or flux) between cohorts. However, since metabolic fluxes cannot be determined directly, researchers typically use constraint-based metabolic network models, customized on post-genomics datasets. The use of random sampling within the feasible region of metabolic networks is becoming more prevalent for comparing these networks. While many algorithms have been proposed and compared for efficiently and uniformly sampling the feasible region of metabolic networks, their impact on the risk of making false discoveries when comparing different samples has not been investigated yet, and no sampling strategy has been so far specifically designed to mitigate the problem. To be able to precisely assess the False Discovery Rate (FDR), in this work we compared different samples obtained from the very same metabolic model. We compared the FDR obtained for different model scales, sample sizes, parameters of the sampling algorithm, and strategies to filter out non-significant variations. To be able to compare the largely used hit-and-run strategy with the much less investigated corner-based strategy, we first assessed the intrinsic capability of current corner-based algorithms and of a newly proposed one to visit all vertices of a constraint-based region. We show that false discoveries can occur at high rates even for large samples of small-scale networks. However, we demonstrate that a statistical test based on the empirical null distribution of Kullback-Leibler divergence can effectively correct for false discoveries. We also show that our proposed corner-based algorithm is more efficient than state-of-the-art alternatives and much less prone to false discoveries than hit-and-run strategies. We report that the differences in the marginal distributions obtained with the two strategies are related to but not fully explained by differences in sample standard deviation, as previously thought. Overall, our study provides insights into the impact of sampling strategies on FDR in metabolic network analysis and offers new guidelines for more robust and reproducible analyses.

Targeting cancer stem cell OXPHOS with tailored ruthenium complexes as a new anti-cancer strategy

BACKGROUND

Previous studies by our group have shown that oxidative phosphorylation (OXPHOS) is the main pathway by which pancreatic cancer stem cells (CSCs) meet their energetic requirements; therefore, OXPHOS represents an Achille's heel of these highly tumorigenic cells. Unfortunately, therapies that target OXPHOS in CSCs are lacking.

METHODS

The safety and anti-CSC activity of a ruthenium complex featuring bipyridine and terpyridine ligands and one coordination labile position (Ru1) were evaluated across primary pancreatic cancer cultures and in vivo, using 8 patient-derived xenografts (PDXs). RNAseq analysis followed by mitochondria-specific molecular assays were used to determine the mechanism of action.

RESULTS

We show that Ru1 is capable of inhibiting CSC OXPHOS function in vitro, and more importantly, it presents excellent anti-cancer activity, with low toxicity, across a large panel of human pancreatic PDXs, as well as in colorectal cancer and osteosarcoma PDXs. Mechanistic studies suggest that this activity stems from Ru1 binding to the D-loop region of the mitochondrial DNA of CSCs, inhibiting OXPHOS complex-associated transcription, leading to reduced mitochondrial oxygen consumption, membrane potential, and ATP production, all of which are necessary for CSCs, which heavily depend on mitochondrial respiration.

CONCLUSIONS

Overall, the coordination complex Ru1 represents not only an exciting new anti-cancer agent, but also a molecular tool to dissect the role of OXPHOS in CSCs. Results indicating that the compound is safe, non-toxic and highly effective in vivo are extremely exciting, and have allowed us to uncover unprecedented mechanistic possibilities to fight different cancer types based on targeting CSC OXPHOS.

Increasing the Pentose Phosphate Pathway Flux to Improve Plasmid DNA Production in Engineered E. coli

The demand of plasmid DNA (pDNA) as a key element for gene therapy products, as well as mRNA and DNA vaccines, is increasing together with the need for more efficient production processes. An engineered E. coli strain lacking the phosphotransferase system and the pyruvate kinase A gene has been shown to produce more pDNA than its parental strain. With the aim of improving pDNA production in the engineered strain, several strategies to increase the flux to the pentose phosphate pathway (PPP) were evaluated. The simultaneous consumption of glucose and glycerol was a simple way to increase the growth rate, pDNA production rate, and supercoiled fraction (SCF). The overexpression of key genes from the PPP also improved pDNA production in glucose, but not in mixtures of glucose and glycerol. Particularly, the gene coding for the glucose 6-phosphate dehydrogenase (G6PDH) strongly improved the SCF, growth rate, and pDNA production rate. A linear relationship between the G6PDH activity and pDNA yield was found. A higher flux through the PPP was confirmed by flux balance analysis, which also estimates relevant differences in fluxes of the tricarboxylic acid cycle. These results are useful for developing further cell engineering strategies to increase pDNA production and quality.

Sustainable production of 2,3,5,6-Tetramethylpyrazine at high titer in engineered Corynebacterium glutamicum

The industrial amino acid production workhorse, Corynebacterium glutamicum naturally produces low levels of 2,3,5,6-tetramethylpyrazine (TMP), a valuable flavor, fragrance, and commodity chemical. Here, we demonstrate TMP production (∼0.8 g L-1) in C. glutamicum type strain ATCC13032 via overexpression of acetolactate synthase and/or α-acetolactate decarboxylase from Lactococcus lactis in CGXII minimal medium supplemented with 40 g L-1 glucose. This engineered strain also demonstrated growth and TMP production when the minimal medium was supplemented with up to 40% (v v-1) hydrolysates derived from ionic liquid-pretreated sorghum biomass. A key objective was to take the fully engineered strain developed in this study and interrogate medium parameters that influence the production of TMP, a critical post-strain engineering optimization. Design of experiments in a high-throughput plate format identified glucose, urea, and their ratio as significant components affecting TMP production. These two components were further optimized using response surface methodology. In the optimized CGXII medium, the engineered strain could produce up to 3.56 g L-1 TMP (4-fold enhancement in titers and 2-fold enhancement in yield, mol mol-1) from 80 g L-1 glucose and 11.9 g L-1 urea in shake flask batch cultivation.

ONE-SENTENCE SUMMARY

Corynebacterium glutamicum was metabolically engineered to produce 2,3,5,6-tetramethylpyrazine followed by a design of experiments approach to optimize medium components for high-titer production.

The metabolic control theory: Its development and its application to mitochondrial oxidative phosphorylation

Metabolic Control Theory (MCT) and Metabolic Control Analysis (MCA) are the two sides, theoretical and experimental, of the measurement of the sensitivity of metabolic networks in the vicinity of a steady state. We will describe the birth and the development of this theory from the first analyses of linear pathways up to a global mathematical theory applicable to any metabolic network. We will describe how the theory, given the global nature of mitochondrial oxidative phosphorylation, solved the problem of what controls mitochondrial ATP synthesis and then how it led to a better understanding of the differential tissue expression of human mitochondrial pathologies and of the heteroplasmy of mitochondrial DNA, leading to the concept of the threshold effect.

Reconstruction and metabolic profiling of the genome-scale metabolic network model of Pseudomonas stutzeri A1501

Pseudomonas stutzeri A1501 is a non-fluorescent denitrifying bacteria that belongs to the gram-negative bacterial group. As a prominent strain in the fields of agriculture and bioengineering, there is still a lack of comprehensive understanding regarding its metabolic capabilities, specifically in terms of central metabolism and substrate utilization. Therefore, further exploration and extensive studies are required to gain a detailed insight into these aspects. This study reconstructed a genome-scale metabolic network model for P. stutzeri A1501 and conducted extensive curations, including correcting energy generation cycles, respiratory chains, and biomass composition. The final model, iQY1018, was successfully developed, covering more genes and reactions and having higher prediction accuracy compared with the previously published model iPB890. The substrate utilization ability of 71 carbon sources was investigated by BIOLOG experiment and was utilized to validate the model quality. The model prediction accuracy of substrate utilization for P. stutzeri A1501 reached 90 %. The model analysis revealed its new ability in central metabolism and predicted that the strain is a suitable chassis for the production of Acetyl CoA-derived products. This work provides an updated, high-quality model of P. stutzeri A1501for further research and will further enhance our understanding of the metabolic capabilities.

Metabolic regulation boosts bioelectricity generation in Zymomonas mobilis microbial fuel cell, surpassing ethanol production

Zymomonas mobilis (Z. mobilis), a bacterium known for its ethanol production capabilities, can also generate electricity by transitioning from ethanol production to electron generation. The purpose of this study is to investigate the ability of Z. mobilis to produce bioelectricity when utilized as a biocatalyst in a single-chamber microbial fuel cell (MFC). Given the bacterium's strong inclination towards ethanol production, a metabolic engineering strategy was devised to identify key reactions responsible for redirecting electrons from ethanol towards electricity generation. To evaluate the electroactivity of cultured Z. mobilis and its ethanol production in the presence of regulators, the reduction of soluble Fe(III) was utilized. Among the regulators tested, CuCl2 demonstrated superior effectiveness. Consequently, the MFC was employed to analyze the electrochemical properties of Z. mobilis using both a minimal and modified medium. By modifying the bacterial medium, the maximum current and power density of the MFC fed with Z. mobilis increased by more than 5.8- and sixfold, respectively, compared to the minimal medium. These findings highlight the significant impact of metabolic redirection in enhancing the performance of MFCs. Furthermore, they establish Z. mobilis as an active electrogenesis microorganism capable of power generation in MFCs.

Epistemology of synthetic biology: a new theoretical framework based on its potential objects and objectives

Synthetic biology is a new research field which attempts to understand, modify, and create new biological entities by adopting a modular and systemic conception of the living organisms. The development of synthetic biology has generated a pluralism of different approaches, bringing together a set of heterogeneous practices and conceptualizations from various disciplines, which can lead to confusion within the synthetic biology community as well as with other biological disciplines. I present in this manuscript an epistemological analysis of synthetic biology in order to better define this new discipline in terms of objects of study and specific objectives. First, I present and analyze the principal research projects developed at the foundation of synthetic biology, in order to establish an overview of the practices in this new emerging discipline. Then, I analyze an important scientometric study on synthetic biology to complete this overview. Afterwards, considering this analysis, I suggest a three-level classification of the object of study for synthetic biology (which are different kinds of living entities that can be built in the laboratory), based on three successive criteria: structural hierarchy, structural origin, functional origin. Finally, I propose three successively linked objectives in which synthetic biology can contribute (where the achievement of one objective led to the development of the other): interdisciplinarity collaboration (between natural, artificial, and theoretical sciences), knowledge of natural living entities (past, present, future, and alternative), pragmatic definition of the concept of "living" (that can be used by biologists in different contexts). Considering this new theoretical framework, based on its potential objects and objectives, I take the position that synthetic biology has not only the potential to develop its own new approach (which includes methods, objects, and objectives), distinct from other subdisciplines in biology, but also the ability to develop new knowledge on living entities.

A genome-scale metabolic reconstruction of soybean and Bradyrhizobium diazoefficiens reveals the cost-benefit of nitrogen fixation

Nitrogen-fixing symbioses allow legumes to thrive in nitrogen-poor soils at the cost of diverting some photoassimilate to their microsymbionts. Effort is being made to bioengineer nitrogen fixation into nonleguminous crops. This requires a quantitative understanding of its energetic costs and the links between metabolic variations and symbiotic efficiency. A whole-plant metabolic model for soybean (Glycine max) with its associated microsymbiont Bradyrhizobium diazoefficiens was developed and applied to predict the cost-benefit of nitrogen fixation with varying soil nitrogen availability. The model predicted a nitrogen-fixation cost of c. 4.13 g C g-1 N, which when implemented into a crop scale model, translated to a grain yield reduction of 27% compared with a non-nodulating plant receiving its nitrogen from the soil. Considering the lower nitrogen content of cereals, the yield cost to a hypothetical N-fixing cereal is predicted to be less than half that of soybean. Soybean growth was predicted to be c. 5% greater when the nodule nitrogen export products were amides versus ureides. This is the first metabolic reconstruction in a tropical crop species that simulates the entire plant and nodule metabolism. Going forward, this model will serve as a tool to investigate carbon use efficiency and key mechanisms within N-fixing symbiosis in a tropical species forming determinate nodules.

Investigating Neuron Degeneration in Huntington's Disease Using RNA-Seq Based Transcriptome Study

Huntington's disease (HD) is a progressive neurodegenerative disorder caused due to a CAG repeat expansion in the huntingtin (HTT) gene. The primary symptoms of HD include motor dysfunction such as chorea, dystonia, and involuntary movements. The primary motor cortex (BA4) is the key brain region responsible for executing motor/movement activities. Investigating patient and control samples from the BA4 region will provide a deeper understanding of the genes responsible for neuron degeneration and help to identify potential markers. Previous studies have focused on overall differential gene expression and associated biological functions. In this study, we illustrate the relationship between variants and differentially expressed genes/transcripts. We identified variants and their associated genes along with the quantification of genes and transcripts. We also predicted the effect of variants on various regulatory activities and found that many variants are regulating gene expression. Variants affecting miRNA and its targets are also highlighted in our study. Co-expression network studies revealed the role of novel genes. Function interaction network analysis unveiled the importance of genes involved in vesicle-mediated transport. From this unified approach, we propose that genes expressed in immune cells are crucial for reducing neuron death in HD.

Mathematical reconstruction of the metabolic network in an in-vitro multiple myeloma model

It is increasingly apparent that cancer cells, in addition to remodelling their metabolism to survive and proliferate, adapt and manipulate the metabolism of other cells. This property may be a telling sign that pre-clinical tumour metabolism studies exclusively utilising in-vitro mono-culture models could prove to be limited for uncovering novel metabolic targets able to translate into clinical therapies. Although this is increasingly recognised, and work towards addressing the issue is becoming routinary much remains poorly understood. For instance, knowledge regarding the biochemical mechanisms through which cancer cells manipulate non-cancerous cell metabolism, and the subsequent impact on their survival and proliferation remains limited. Additionally, the variations in these processes across different cancer types and progression stages, and their implications for therapy, also remain largely unexplored. This study employs an interdisciplinary approach that leverages the predictive power of mathematical modelling to enrich experimental findings. We develop a functional multicellular in-silico model that facilitates the qualitative and quantitative analysis of the metabolic network spawned by an in-vitro co-culture model of bone marrow mesenchymal stem- and myeloma cell lines. To procure this model, we devised a bespoke human genome constraint-based reconstruction workflow that combines aspects from the legacy mCADRE & Metabotools algorithms, the novel redHuman algorithm, along with 13C-metabolic flux analysis. Our workflow transforms the latest human metabolic network matrix (Recon3D) into two cell-specific models coupled with a metabolic network spanning a shared growth medium. When cross-validating our in-silico model against the in-vitro model, we found that the in-silico model successfully reproduces vital metabolic behaviours of its in-vitro counterpart; results include cell growth predictions, respiration rates, as well as support for observations which suggest cross-shuttling of redox-active metabolites between cells.

Substrate type and CO2 addition significantly influence succinic acid production of Basfia succiniciproducens

Metabolic engineering has shown that optimizing metabolic pathways' fluxes for industrial purposes requires a methodical approach. Accordingly, in this study, in silico metabolic modeling was utilized to characterize the lesser-known strain Basfia succiniciproducens under different environmental conditions, followed by the use of industrially relevant substrates for succinic acid synthesis. Based on RT-qPCR carried out in flask experiments, we discovered a relatively large difference in the expression levels of ldhA gene compared to glucose in both xylose and glycerol cultures. In bioreactor-scale fermentations, the impact of different gas phases (CO2, CO2/AIR) on biomass yield, substrate consumption, and metabolite profiles was also investigated. In the case of glycerol, the addition of CO2 increased biomass as well as target product formation, while using CO2/AIR gas phase resulted in higher target product yield (0.184 mM⋅mM-1). In case of xylose, using CO2 alone would result in higher succinic acid production (0.277 mM⋅mM-1). The promising rumen bacteria, B. succiniciproducens, has shown to be suitable for succinic acid production from both xylose and glycerol. As a result, our findings present new opportunities for broadening the range of raw materials used in this significant biochemical process. Our study also sheds light on fermentation parameter optimization for this strain, namely that, CO2/AIR supply has a positive effect on target product formation.

Uses of Multi-Objective Flux Analysis for Optimization of Microbial Production of Secondary Metabolites

Secondary metabolites are not essential for the growth of microorganisms, but they play a critical role in how microbes interact with their surroundings. In addition to this important ecological role, secondary metabolites also have a variety of agricultural, medicinal, and industrial uses, and thus the examination of secondary metabolism of plants and microbes is a growing scientific field. While the chemical production of certain secondary metabolites is possible, industrial-scale microbial production is a green and economically attractive alternative. This is even more true, given the advances in bioengineering that allow us to alter the workings of microbes in order to increase their production of compounds of interest. This type of engineering requires detailed knowledge of the "chassis" organism's metabolism. Since the resources and the catalytic capacity of enzymes in microbes is finite, it is important to examine the tradeoffs between various bioprocesses in an engineered system and alter its working in a manner that minimally perturbs the robustness of the system while allowing for the maximum production of a product of interest. The in silico multi-objective analysis of metabolism using genome-scale models is an ideal method for such examinations.

Identification of gene function based on models capturing natural variability of Arabidopsis thaliana lipid metabolism

Lipids play fundamental roles in regulating agronomically important traits. Advances in plant lipid metabolism have until recently largely been based on reductionist approaches, although modulation of its components can have system-wide effects. However, existing models of plant lipid metabolism provide lumped representations, hindering detailed study of component modulation. Here, we present the Plant Lipid Module (PLM) which provides a mechanistic description of lipid metabolism in the Arabidopsis thaliana rosette. We demonstrate that the PLM can be readily integrated in models of A. thaliana Col-0 metabolism, yielding accurate predictions (83%) of single lethal knock-outs and 75% concordance between measured transcript and predicted flux changes under extended darkness. Genome-wide associations with fluxes obtained by integrating the PLM in diel condition- and accession-specific models identify up to 65 candidate genes modulating A. thaliana lipid metabolism. Using mutant lines, we validate up to 40% of the candidates, paving the way for identification of metabolic gene function based on models capturing natural variability in metabolism.

Genome-scale metabolic model led engineering of Nothapodytes nimmoniana plant cells for high camptothecin production

Camptothecin (CPT) is a vital monoterpene indole alkaloid used in anti-cancer therapeutics. It is primarily derived from Camptotheca acuminata and Nothapodytes nimmoniana plants that are indigenous to Southeast Asia. Plants have intricate metabolic networks and use them to produce secondary metabolites such as CPT, which is a prerequisite for rational metabolic engineering design to optimize their production. By reconstructing metabolic models, we can predict plant metabolic behavior, facilitating the selection of suitable approaches and saving time, cost, and energy, over traditional hit and trial experimental approaches. In this study, we reconstructed a genome-scale metabolic model for N. nimmoniana (NothaGEM iSM1809) and curated it using experimentally obtained biochemical data. We also used in silico tools to identify and rank suitable enzyme targets for overexpression and knockout to maximize camptothecin production. The predicted over-expression targets encompass enzymes involved in the camptothecin biosynthesis pathway, including strictosidine synthase and geraniol 10-hydroxylase, as well as targets related to plant metabolism, such as amino acid biosynthesis and the tricarboxylic acid cycle. The top-ranked knockout targets included reactions responsible for the formation of folates and serine, as well as the conversion of acetyl CoA and oxaloacetate to malate and citrate. One of the top-ranked overexpression targets, strictosidine synthase, was chosen to generate metabolically engineered cell lines of N. nimmoniana using Agrobacterium tumefaciens-mediated transformation. The transformed cell line showed a 5-fold increase in camptothecin production, with a yield of up to 5 µg g-1.

The genome-scale metabolic model for the purple non-sulfur bacterium Rhodopseudomonas palustris Bis A53 accurately predicts phenotypes under chemoheterotrophic, chemoautotrophic, photoheterotrophic, and photoautotrophic growth conditions

The purple non-sulfur bacterium Rhodopseudomonas palustris is recognized as a critical microorganism in the nitrogen and carbon cycle and one of the most common members in wastewater treatment communities. This bacterium is metabolically extremely versatile. It is capable of heterotrophic growth under aerobic and anaerobic conditions, but also able to grow photoautotrophically as well as mixotrophically. Therefore R. palustris can adapt to multiple environments and establish commensal relationships with other organisms, expressing various enzymes supporting degradation of amino acids, carbohydrates, nucleotides, and complex polymers. Moreover, R. palustris can degrade a wide range of pollutants under anaerobic conditions, e.g., aromatic compounds such as benzoate and caffeate, enabling it to thrive in chemically contaminated environments. However, many metabolic mechanisms employed by R. palustris to breakdown and assimilate different carbon and nitrogen sources under chemoheterotrophic or photoheterotrophic conditions remain unknown. Systems biology approaches, such as metabolic modeling, have been employed extensively to unravel complex mechanisms of metabolism. Previously, metabolic models have been reconstructed to study selected capabilities of R. palustris under limited experimental conditions. Here, we developed a comprehensive metabolic model (M-model) for R. palustris Bis A53 (iDT1294) consisting of 2,721 reactions, 2,123 metabolites, and comprising 1,294 genes. We validated the model using high-throughput phenotypic, physiological, and kinetic data, testing over 350 growth conditions. iDT1294 achieved a prediction accuracy of 90% for growth with various carbon and nitrogen sources and close to 80% for assimilation of aromatic compounds. Moreover, the M-model accurately predicts dynamic changes of growth and substrate consumption rates over time under nine chemoheterotrophic conditions and demonstrated high precision in predicting metabolic changes between photoheterotrophic and photoautotrophic conditions. This comprehensive M-model will help to elucidate metabolic processes associated with the assimilation of multiple carbon and nitrogen sources, anoxygenic photosynthesis, aromatic compound degradation, as well as production of molecular hydrogen and polyhydroxybutyrate.

TranSyT, an innovative framework for identifying transport systems

MOTIVATION

The importance and rate of development of genome-scale metabolic models have been growing for the last few years, increasing the demand for software solutions that automate several steps of this process. However, since TRIAGE's release, software development for the automatic integration of transport reactions into models has stalled.

RESULTS

Here, we present the Transport Systems Tracker (TranSyT). Unlike other transport systems annotation software, TranSyT does not rely on manual curation to expand its internal database, which is derived from highly curated records retrieved from the Transporters Classification Database and complemented with information from other data sources. TranSyT compiles information regarding transporter families and proteins, and derives reactions into its internal database, making it available for rapid annotation of complete genomes. All transport reactions have GPR associations and can be exported with identifiers from four different metabolite databases. TranSyT is currently available as a plugin for merlin v4.0 and an app for KBase.

AVAILABILITY AND IMPLEMENTATION

TranSyT web service: https://transyt.bio.di.uminho.pt/; GitHub for the tool: https://github.com/BioSystemsUM/transyt; GitHub with examples and instructions to run TranSyT: https://github.com/ecunha1996/transyt_paper.

A new metabolic model of Drosophila melanogaster and the integrative analysis of Parkinson's disease

High conservation of the disease-associated genes between flies and humans facilitates the common use of Drosophila melanogaster to study metabolic disorders under controlled laboratory conditions. However, metabolic modeling studies are highly limited for this organism. We here report a comprehensively curated genome-scale metabolic network model of Drosophila using an orthology-based approach. The gene coverage and metabolic information of the draft model derived from a reference human model were expanded via Drosophila-specific KEGG and MetaCyc databases, with several curation steps to avoid metabolic redundancy and stoichiometric inconsistency. Furthermore, we performed literature-based curations to improve gene-reaction associations, subcellular metabolite locations, and various metabolic pathways. The performance of the resulting Drosophila model (8,230 reactions, 6,990 metabolites, and 2,388 genes), iDrosophila1 (https://github.com/SysBioGTU/iDrosophila), was assessed using flux balance analysis in comparison with the other currently available fly models leading to superior or comparable results. We also evaluated the transcriptome-based prediction capacity of iDrosophila1, where differential metabolic pathways during Parkinson's disease could be successfully elucidated. Overall, iDrosophila1 is promising to investigate system-level metabolic alterations in response to genetic and environmental perturbations.

Mitigating biomass composition uncertainties in flux balance analysis using ensemble representations

The biomass equation is a critical component in genome-scale metabolic models (GEMs): it is used as the de facto objective function in flux balance analysis (FBA). This equation accounts for the quantities of all known biomass precursors that are required for cell growth based on the macromolecular and monomer compositions measured at certain conditions. However, it is often reported that the macromolecular composition of cells could change across different environmental conditions and thus the use of the same single biomass equation in FBA, under multiple conditions, is questionable. Herein, we first investigated the qualitative and quantitative variations of macromolecular compositions of three representative host organisms, Escherichia coli, Saccharomyces cerevisiae and Cricetulus griseus, across different environmental/genetic variations. While macromolecular building blocks such as RNA, protein, and lipid composition vary notably, changes in fundamental biomass monomer units such as nucleotides and amino acids are not appreciable. We also observed that flux predictions through FBA is quite sensitive to macromolecular compositions but not the monomer compositions. Based on these observations, we propose ensemble representations of biomass equation in FBA to account for the natural variation of cellular constituents. Such ensemble representations of biomass better predicted the flux through anabolic reactions as it allows for the flexibility in the biosynthetic demands of the cells. The current study clearly highlights that certain component of the biomass equation indeed vary across different conditions, and the ensemble representation of biomass equation in FBA by accounting for such natural variations could avoid inaccuracies that may arise from in silico simulations.

Integrating Omics Data in Genome-Scale Metabolic Modeling: A Methodological Perspective for Precision Medicine

Recent advancements in omics technologies have generated a wealth of biological data. Integrating these data within mathematical models is essential to fully leverage their potential. Genome-scale metabolic models (GEMs) provide a robust framework for studying complex biological systems. GEMs have significantly contributed to our understanding of human metabolism, including the intrinsic relationship between the gut microbiome and the host metabolism. In this review, we highlight the contributions of GEMs and discuss the critical challenges that must be overcome to ensure their reproducibility and enhance their prediction accuracy, particularly in the context of precision medicine. We also explore the role of machine learning in addressing these challenges within GEMs. The integration of omics data with GEMs has the potential to lead to new insights, and to advance our understanding of molecular mechanisms in human health and disease.

Genome-scale metabolic modeling and in silico analysis of opportunistic skin pathogen Cutibacterium acnes

Cutibacterium acnes, one of the most abundant skin microbes found in the sebaceous gland, is known to contribute to the development of acne vulgaris when its strains become imbalanced. The current limitations of acne treatment using antibiotics have caused an urgent need to develop a systematic strategy for selectively targeting C. acnes, which can be achieved by characterizing their cellular behaviors under various skin environments. To this end, we developed a genome-scale metabolic model (GEM) of virulent C. acnes, iCA843, based on the genome information of a relevant strain from ribotype 5 to comprehensively understand the pathogenic traits of C. acnes in the skin environment. We validated the model qualitatively by demonstrating its accuracy prediction of propionate and acetate production patterns, which were consistent with experimental observations. Additionally, we identified unique biosynthetic pathways for short-chain fatty acids in C. acnes compared to other GEMs of acne-inducing skin pathogens. By conducting constraint-based flux analysis under endogenous carbon sources in human skin, we discovered that the Wood-Werkman cycle is highly activated under acnes-associated skin condition for the regeneration of NAD, resulting in enhanced propionate production. Finally, we proposed potential anti-C. acnes targets by using the model-guided systematic framework based on gene essentiality analysis and protein sequence similarity search with abundant skin microbiome taxa.

A genome-scale dynamic constraint-based modelling (gDCBM) framework predicts growth dynamics, medium composition and intracellular flux distributions in CHO clonal variations

Optimizing mammalian cell growth and bioproduction is a tedious task. However, due to the inherent complexity of eukaryotic cells, heuristic experimental approaches such as, metabolic engineering and bioprocess design, are frequently integrated with mathematical models of cell culture to improve biological process efficiency and find paths for improvement. Constraint-based metabolic models have evolved over the last two decades to be used for dynamic modelling in addition to providing a linear description of steady-state metabolic systems. Formulation and implementation of the underlying optimization problems require special attention to the model's performance and feasibility, lack of defects in the definition of system components, and consideration of optimal alternate solutions, in addition to processing power limitations. Here, the time-resolved dynamics of a genome-scale metabolic network of Chinese hamster ovary (CHO) cell metabolism are shown using a genome-scale dynamic constraint-based modelling framework (gDCBM). The metabolic network was adapted from a reference model of CHO genome-scale metabolic model (GSMM), iCHO_DG44_v1, and dynamic restrictions were imposed to its exchange fluxes based on experimental results. We used this framework for predicting physiological changes in CHO clonal variants. Because of the methodical creation of the components for the flux balance analysis optimization problem and the integration of a switch time, this model can generate sequential predictions of intracellular fluxes during growth and non-growth phases (per hour of culture time) and transparently reveal the shortcomings in such practice. As a result of the differences exploited by various clones, we can understand the relevance of changes in intracellular flux distribution and exometabolomics. The integration of various omics data into the given gDCBM framework, as well as the reductionist analysis of the model, can further help bioprocess optimization.

High throughput 13C-metabolic flux analysis of 3-hydroxypropionic acid producing Pichia pastoris reveals limited availability of acetyl-CoA and ATP due to tight control of the glycolytic flux

BACKGROUND

Production of 3-hydroxypropionic acid (3-HP) through the malonyl-CoA pathway has yielded promising results in Pichia pastoris (Komagataella phaffii), demonstrating the potential of this cell factory to produce this platform chemical and other acetyl-CoA-derived products using glycerol as a carbon source. However, further metabolic engineering of the original P. pastoris 3-HP-producing strains resulted in unexpected outcomes, e.g., significantly lower product yield and/or growth rate. To gain an understanding on the metabolic constraints underlying these observations, the fluxome (metabolic flux phenotype) of ten 3-HP-producing P. pastoris strains has been characterized using a high throughput ¹³C-metabolic flux analysis platform. Such platform enabled the operation of an optimised workflow to obtain comprehensive maps of the carbon flux distribution in the central carbon metabolism in a parallel-automated manner, thereby accelerating the time-consuming strain characterization step in the design-build-test-learn cycle for metabolic engineering of P. pastoris.

RESULTS

We generated detailed maps of the carbon fluxes in the central carbon metabolism of the 3-HP producing strain series, revealing the metabolic consequences of different metabolic engineering strategies aimed at improving NADPH regeneration, enhancing conversion of pyruvate into cytosolic acetyl-CoA, or eliminating by-product (arabitol) formation. Results indicate that the expression of the POS5 NADH kinase leads to a reduction in the fluxes of the pentose phosphate pathway reactions, whereas an increase in the pentose phosphate pathway fluxes was observed when the cytosolic acetyl-CoA synthesis pathway was overexpressed. Results also show that the tight control of the glycolytic flux hampers cell growth due to limited acetyl-CoA biosynthesis. When the cytosolic acetyl-CoA synthesis pathway was overexpressed, the cell growth increased, but the product yield decreased due to higher growth-associated ATP costs. Finally, the six most relevant strains were also cultured at pH 3.5 to assess the effect of a lower pH on their fluxome. Notably, similar metabolic fluxes were observed at pH 3.5 compared to the reference condition at pH 5.

CONCLUSIONS

This study shows that existing fluoxomics workflows for high-throughput analyses of metabolic phenotypes can be adapted to investigate P. pastoris, providing valuable information on the impact of genetic manipulations on the metabolic phenotype of this yeast. Specifically, our results highlight the metabolic robustness of P. pastoris's central carbon metabolism when genetic modifications are made to increase the availability of NADPH and cytosolic acetyl-CoA. Such knowledge can guide further metabolic engineering of these strains. Moreover, insights into the metabolic adaptation of P. pastoris to an acidic pH have also been obtained, showing the capability of the fluoxomics workflow to assess the metabolic impact of environmental changes.

A systematic overexpression approach reveals native targets to increase squalene production in Synechocystis sp. PCC 6803

Cyanobacteria are a promising platform for the production of the triterpene squalene (C30), a precursor for all plant and animal sterols, and a highly attractive intermediate towards triterpenoids, a large group of secondary plant metabolites. Synechocystis sp. PCC 6803 natively produces squalene from CO2 through the MEP pathway. Based on the predictions of a constraint-based metabolic model, we took a systematic overexpression approach to quantify native Synechocystis gene's impact on squalene production in a squalene-hopene cyclase gene knock-out strain (Δshc). Our in silico analysis revealed an increased flux through the Calvin-Benson-Bassham cycle in the Δshc mutant compared to the wildtype, including the pentose phosphate pathway, as well as lower glycolysis, while the tricarboxylic acid cycle predicted to be downregulated. Further, all enzymes of the MEP pathway and terpenoid synthesis, as well as enzymes from the central carbon metabolism, Gap2, Tpi and PyrK, were predicted to positively contribute to squalene production upon their overexpression. Each identified target gene was integrated into the genome of Synechocystis Δshc under the control of the rhamnose-inducible promoter Prha. Squalene production was increased in an inducer concentration dependent manner through the overexpression of most predicted genes, which are genes of the MEP pathway, ispH, ispE, and idi, leading to the greatest improvements. Moreover, we were able to overexpress the native squalene synthase gene (sqs) in Synechocystis Δshc, which reached the highest production titer of 13.72 mg l-1 reported for squalene in Synechocystis sp. PCC 6803 so far, thereby providing a promising and sustainable platform for triterpene production.

Functional comparison of metabolic networks across species

Metabolic phenotypes are pivotal for many areas, but disentangling how evolutionary history and environmental adaptation shape these phenotypes is an open problem. Especially for microbes, which are metabolically diverse and often interact in complex communities, few phenotypes can be determined directly. Instead, potential phenotypes are commonly inferred from genomic information, and rarely were model-predicted phenotypes employed beyond the species level. Here, we propose sensitivity correlations to quantify similarity of predicted metabolic network responses to perturbations, and thereby link genotype and environment to phenotype. We show that these correlations provide a consistent functional complement to genomic information by capturing how network context shapes gene function. This enables, for example, phylogenetic inference across all domains of life at the organism level. For 245 bacterial species, we identify conserved and variable metabolic functions, elucidate the quantitative impact of evolutionary history and ecological niche on these functions, and generate hypotheses on associated metabolic phenotypes. We expect our framework for the joint interpretation of metabolic phenotypes, evolution, and environment to help guide future empirical studies.

A Multi-Level Systems Biology Analysis of Aldrin's Metabolic Effects on Prostate Cancer Cells

Although numerous studies support a dose-effect relationship between Endocrine disruptors (EDs) and the progression and malignancy of tumors, the impact of a chronic exposure to non-lethal concentrations of EDs in cancer remains unknown. More specifically, a number of studies have reported the impact of Aldrin on a variety of cancer types, including prostate cancer. In previous studies, we demonstrated the induction of the malignant phenotype in DU145 prostate cancer (PCa) cells after a chronic exposure to Aldrin (an ED). Proteins are pivotal in the regulation and control of a variety of cellular processes. However, the mechanisms responsible for the impact of ED on PCa and the role of proteins in this process are not yet well understood. Here, two complementary computational approaches have been employed to investigate the molecular processes underlying the acquisition of malignancy in prostate cancer. First, the metabolic reprogramming associated with the chronic exposure to Aldrin in DU145 cells was studied by integrating transcriptomics and metabolomics via constraint-based metabolic modeling. Second, gene set enrichment analysis was applied to determine (i) altered regulatory pathways and (ii) the correlation between changes in the transcriptomic profile of Aldrin-exposed cells and tumor progression in various types of cancer. Experimental validation confirmed predictions revealing a disruption in metabolic and regulatory pathways. This alteration results in the modification of protein levels crucial in regulating triacylglyceride/cholesterol, linked to the malignant phenotype observed in Aldrin-exposed cells.

Loss of the Kidney Urate Transporter, Urat1, Leads to Disrupted Redox Homeostasis in Mice

High uric acid is associated with gout, hypertension, metabolic syndrome, cardiovascular disease, and kidney disease. URAT1 (SLC22A12), originally discovered in mice as Rst, is generally considered a very selective uric acid transporter compared to other closely-related kidney uric acid transporters such as OAT1 (SLC22A6, NKT) and OAT3 (SLC22A8). While the role of URAT1 in regulating human uric acid is well-established, in recent studies the gene has been linked to redox regulation in flies as well as progression of renal cell carcinoma. We have now identified over twenty metabolites in the Urat1 knockout that are generally distinct from metabolites accumulating in the Oat1 and Oat3 knockout mice, with distinct molecular properties as revealed by chemoinformatics and machine learning analysis. These metabolites are involved in seemingly disparate aspects of cellular metabolism, including pyrimidine, fatty acid, and amino acid metabolism. However, through integrative systems metabolic analysis of the transcriptomic and metabolomic data using a human metabolic reconstruction to build metabolic genome-scale models (GEMs), the cellular response to loss of Urat1/Rst revealed compensatory processes related to reactive oxygen species handling and maintaining redox state balances via Vitamin C metabolism and cofactor charging reactions. These observations are consistent with the increasingly appreciated role of the antioxidant properties of uric acid. Collectively, the results highlight the role of Urat1/Rst as a transporter strongly tied to maintaining redox homeostasis, with implications for metabolic side effects from drugs that block its function.

Highlighting the potential of Synechococcus elongatus PCC 7942 as platform to produce α-linolenic acid through an updated genome-scale metabolic modeling

Cyanobacteria are prokaryotic organisms that capture energy from sunlight using oxygenic photosynthesis and transform CO2 into products of interest such as fatty acids. Synechococcus elongatus PCC 7942 is a model cyanobacterium efficiently engineered to accumulate high levels of omega-3 fatty acids. However, its exploitation as a microbial cell factory requires a better knowledge of its metabolism, which can be approached by using systems biology tools. To fulfill this objective, we worked out an updated, more comprehensive, and functional genome-scale model of this freshwater cyanobacterium, which was termed iMS837. The model includes 837 genes, 887 reactions, and 801 metabolites. When compared with previous models of S. elongatus PCC 7942, iMS837 is more complete in key physiological and biotechnologically relevant metabolic hubs, such as fatty acid biosynthesis, oxidative phosphorylation, photosynthesis, and transport, among others. iMS837 shows high accuracy when predicting growth performance and gene essentiality. The validated model was further used as a test-bed for the assessment of suitable metabolic engineering strategies, yielding superior production of non-native omega-3 fatty acids such as α-linolenic acid (ALA). As previously reported, the computational analysis demonstrated that fabF overexpression is a feasible metabolic target to increase ALA production, whereas deletion and overexpression of fabH cannot be used for this purpose. Flux scanning based on enforced objective flux, a strain-design algorithm, allowed us to identify not only previously known gene overexpression targets that improve fatty acid synthesis, such as Acetyl-CoA carboxylase and β-ketoacyl-ACP synthase I, but also novel potential targets that might lead to higher ALA yields. Systematic sampling of the metabolic space contained in iMS837 identified a set of ten additional knockout metabolic targets that resulted in higher ALA productions. In silico simulations under photomixotrophic conditions with acetate or glucose as a carbon source boosted ALA production levels, indicating that photomixotrophic nutritional regimens could be potentially exploited in vivo to improve fatty acid production in cyanobacteria. Overall, we show that iMS837 is a powerful computational platform that proposes new metabolic engineering strategies to produce biotechnologically relevant compounds, using S. elongatus PCC 7942 as non-conventional microbial cell factory.

Improvement of Lutein Production in Auxenochlorella protothecoides Using Its Genome-Scale Metabolic Model and a System-Oriented Approach

Lutein is a valuable metabolite widely used in the food, pharmaceutical, cosmetic, and aquaculture industries. Marigold flowers are the most common source of commercial lutein, but cultivation area, weather conditions, and high manpower costs are among the disadvantages of lutein production from marigold flowers. Microalgae are an excellent alternative to plant sources of lutein as they do not have the limitations of plant extraction. Auxenochlorella protothecoides is a promising candidate for commercial production of lutein. In the present research, a genome-scale metabolic model was applied to introduce some strategies to improve lutein production in A. protothecoides. The effective reactions to improve lutein production were determined based on analysis of multiple optimal solutions. The enzymatic regulators of candidate reactions were identified using the BRENDA database. The effect of 13 activators was investigated experimentally. Our results showed that sodium citrate has the greatest effect on lutein production, so it was introduced as the most effective compound for increasing lutein production by A. protothecoides.

Functional evaluation of non-oxidative glycolysis in Escherichia coli in the stationary phase under microaerobic conditions

In microbial bioproduction, CO₂ emissions via pyruvate dehydrogenase in the Embden-Meyerhof pathway, which converts glucose to acetyl-CoA, is one of the challenges for enhancing carbon yield. The synthetic non-oxidative glycolysis (NOG) pathway transforms glucose into three acetyl-CoA molecules without CO₂ emission, making it an attractive module for metabolic engineering. Because the NOG pathway generates no ATP and NADH, it is expected to use a resting cell reaction. Therefore, it is important to characterize the feasibility of the NOG pathway during stationary phase. Here, we experimentally evaluated the in vivo metabolic flow of the NOG pathway in Escherichia coli. An engineered strain was constructed by introducing phosphoketolase from Bifidobacterium adolescentis into E. coli and by deleting competitive reactions. When the strain was cultured in magnesium-starved medium under microaerobic conditions, the carbon yield of acetate, an end-product of the NOG pathway, was six times higher than that of the control strain harboring an empty vector. Based on the mass balance constraints, the NOG flux was estimated to be between 2.89 and 4.64 mmol g^-1 h^-1, suggesting that the engineered cells can convert glucose through the NOG pathway with enough activity for bioconversion. Furthermore, to expand the application potential of NOG pathway-implemented strains, the theoretical maximum yields of various useful compounds were calculated using flux balance analysis. This suggests that the theoretical maximum yields of not only acetate but also lactam compounds can be increased by introducing the NOG pathway. This information will help in future applications of the NOG pathway.

Predicting stress response and improved protein overproduction in Bacillus subtilis

Bacillus subtilis is a well-characterized microorganism and a model for the study of Gram-positive bacteria. The bacterium can produce proteins at high densities and yields, which has made it valuable for industrial bioproduction. Like other cell factories, metabolic modeling of B. subtilis has discovered ways to optimize its metabolism toward various applications. The first genome-scale metabolic model (M-model) of B. subtilis was published more than a decade ago and has been applied extensively to understand metabolism, to predict growth phenotypes, and served as a template to reconstruct models for other Gram-positive bacteria. However, M-models are ill-suited to simulate the production and secretion of proteins as well as their proteomic response to stress. Thus, a new generation of metabolic models, known as metabolism and gene expression models (ME-models), has been initiated. Here, we describe the reconstruction and validation of a ME model of B. subtilis, iJT964-ME. This model achieved higher performance scores on the prediction of gene essentiality as compared to the M-model. We successfully validated the model by integrating physiological and omics data associated with gene expression responses to ethanol and salt stress. The model further identified the mechanism by which tryptophan synthesis is upregulated under ethanol stress. Further, we employed iJT964-ME to predict amylase production rates under two different growth conditions. We analyzed these flux distributions and identified key metabolic pathways that permitted the increase in amylase production. Models like iJT964-ME enable the study of proteomic response to stress and the illustrate the potential for optimizing protein production in bacteria.

Systematic evaluation of genome-wide metabolic landscapes in lactic acid bacteria reveals diet- and strain-specific probiotic idiosyncrasies

Lactic acid bacteria (LAB) are well known to elicit health benefits in humans, but their functional metabolic landscapes remain unexplored. Here, we analyze differences in growth, intestinal persistence, and postbiotic biosynthesis of six representative LAB and their interactions with 15 gut bacteria under 11 dietary regimes by combining multi-omics and in silico modeling. We confirmed predictions on short-term persistence of LAB and their interactions with commensals using cecal microbiome abundance and spent-medium experiments. Our analyses indicate that probiotic attributes are both diet and species specific and cannot be solely explained using genomics. For example, although both Lacticaseibacillus casei and Lactiplantibacillus plantarum encode similarly sized genomes with diverse capabilities, L. casei exhibits a more desirable phenotype. In addition, "high-fat/low-carb" diets more likely lead to detrimental outcomes for most LAB. Collectively, our results highlight that probiotics are not "one size fits all" health supplements and lay the foundation for personalized probiotic design.

Model-based driving mechanism analysis for butyric acid production in Clostridium tyrobutyricum

Background

Butyric acid, an essential C4 platform chemical, is widely used in food, pharmaceutical, and animal feed industries. Clostridium tyrobutyricum is the most promising microorganism for industrial bio-butyrate production. However, the metabolic driving mechanism for butyrate synthesis was still not profoundly studied.

Results

This study reports a first-generation genome-scale model (GEM) for C. tyrobutyricum, which provides a comprehensive and systematic analysis for the butyrate synthesis driving mechanisms. Based on the analysis in silico, an energy conversion system, which couples the proton efflux with butyryl-CoA transformation by two redox loops of ferredoxin, could be the main driving force for butyrate synthesis. For verifying the driving mechanism, a hydrogenase (HydA) expression was perturbed by inducible regulation and knockout. The results showed that HydA deficiency significantly improved the intracellular NADH/NAD+ rate, decreased acetate accumulation (63.6% in serum bottle and 58.1% in bioreactor), and improved the yield of butyrate (26.3% in serum bottle and 34.5% in bioreactor). It was in line with the expectation based on the energy conversion coupling driving mechanism.

Conclusions

This work show that the first-generation GEM and coupling metabolic analysis effectively promoted in-depth understanding of the metabolic driving mechanism in C. tyrobutyricum and provided a new insight for tuning metabolic flux direction in Clostridium chassis cells.

Drug transporters OAT1 and OAT3 have specific effects on multiple organs and gut microbiome as revealed by contextualized metabolic network reconstructions

In vitro and in vivo studies have established the organic anion transporters OAT1 (SLC22A6, NKT) and OAT3 (SLC22A8) among the main multi-specific "drug" transporters. They also transport numerous endogenous metabolites, raising the possibility of drug-metabolite interactions (DMI). To help understand the role of these drug transporters on metabolism across scales ranging from organ systems to organelles, a formal multi-scale analysis was performed. Metabolic network reconstructions of the omics-alterations resulting from Oat1 and Oat3 gene knockouts revealed links between the microbiome and human metabolism including reactions involving small organic molecules such as dihydroxyacetone, alanine, xanthine, and p-cresol-key metabolites in independent pathways. Interestingly, pairwise organ-organ interactions were also disrupted in the two Oat knockouts, with altered liver, intestine, microbiome, and skin-related metabolism. Compared to older models focused on the "one transporter-one organ" concept, these more sophisticated reconstructions, combined with integration of a multi-microbial model and more comprehensive metabolomics data for the two transporters, provide a considerably more complex picture of how renal "drug" transporters regulate metabolism across the organelle (e.g. endoplasmic reticulum, Golgi, peroxisome), cellular, organ, inter-organ, and inter-organismal scales. The results suggest that drugs interacting with OAT1 and OAT3 can have far reaching consequences on metabolism in organs (e.g. skin) beyond the kidney. Consistent with the Remote Sensing and Signaling Theory (RSST), the analysis demonstrates how transporter-dependent metabolic signals mediate organ crosstalk (e.g., gut-liver-kidney) and inter-organismal communication (e.g., gut microbiome-host).

AHR is a master regulator of diverse pathways in endogenous metabolism

The aryl hydrocarbon receptor (AHR) is a transcription factor with roles in detoxification, development, immune response, chronic kidney disease and other syndromes. It regulates the expression of drug transporters and drug metabolizing enzymes in a proposed Remote Sensing and Signaling Network involved in inter-organ communication via metabolites and signaling molecules. Here, we use integrated omics approaches to analyze its contributions to metabolism across multiple scales from the organ to the organelle. Global metabolomics analysis of Ahr^-/- mice revealed the role of AHR in the regulation of 290 metabolites involved in many biochemical pathways affecting fatty acids, bile acids, gut microbiome products, antioxidants, choline derivatives, and uremic toxins. Chemoinformatics analysis suggest that AHR plays a role in determining the hydrophobicity of metabolites and perhaps their transporter-mediated movement into and out of tissues. Of known AHR ligands, indolepropionate was the only significantly altered molecule, and it activated AHR in both human and murine cells. To gain a deeper biological understanding of AHR, we employed genome scale metabolic reconstruction to integrate knockout transcriptomics and metabolomics data, which indicated a role for AHR in regulation of organic acids and redox state. Together, the results indicate a central role of AHR in metabolism and signaling between multiple organs and across multiple scales.

Metabolic control analysis enables rational improvement of E. coli L-tryptophan producers but methylglyoxal formation limits glycerol-based production

BACKGROUND

Although efficient L-tryptophan production using engineered Escherichia coli is established from glucose, the use of alternative carbon sources is still very limited. Through the application of glycerol as an alternate, a more sustainable substrate (by-product of biodiesel preparation), the well-studied intracellular glycolytic pathways are rerouted, resulting in the activity of different intracellular control sites and regulations, which are not fully understood in detail. Metabolic analysis was applied to well-known engineered E. coli cells with 10 genetic modifications. Cells were withdrawn from a fed-batch production process with glycerol as a carbon source, followed by metabolic control analysis (MCA). This resulted in the identification of several additional enzymes controlling the carbon flux to L-tryptophan.

RESULTS

These controlling enzyme activities were addressed stepwise by the targeted overexpression of 4 additional enzymes (trpC, trpB, serB, aroB). Their efficacy regarding L-tryptophan productivity was evaluated under consistent fed-batch cultivation conditions. Although process comparability was impeded by process variances related to a temporal, unpredictable break-off in L-tryptophan production, process improvements of up to 28% with respect to the L-tryptophan produced were observed using the new producer strains. The intracellular effects of these targeted genetic modifications were revealed by metabolic analysis in combination with MCA and expression analysis. Furthermore, it was discovered that the E. coli cells produced the highly toxic metabolite methylglyoxal (MGO) during the fed-batch process. A closer look at the MGO production and detoxification on the metabolome, fluxome, and transcriptome level of the engineered E. coli indicated that the highly toxic metabolite plays a critical role in the production of aromatic amino acids with glycerol as a carbon source.

CONCLUSIONS

A detailed process analysis of a new L-tryptophan producer strain revealed that several of the 4 targeted genetic modifications of the E. coli L-tryptophan producer strain proved to be effective, and, for others, new engineering approaches could be derived from the results. As a starting point for further strain and process optimization, the up-regulation of MGO detoxifying enzymes and a lowering of the feeding rate during the last third of the cultivation seems reasonable.