Metabolic Model Research Articles

Modification and analysis of context-specific genome-scale metabolic models: methane-utilizing microbial chassis as a case study.

The interrogation of various types of data is a routine strategy to explore the relationship between genotype and phenotype. An efficient approach for integrating and cross-comparing experimental multi-scale data in the context of whole-genome-based metabolic network reconstruction becomes a powerful tool that facilitates fundamental and applied research discoveries. The present study describes the reconstruction of a context-specific (CS) model for the methane-utilizing bacterium, Methylotuvimicrobium alcaliphilum 20ZR. M. alcaliphilum 20ZR is becoming an attractive microbial platform for the production of biofuels, chemicals, pharmaceuticals, and bio-sorbents for capturing atmospheric methane. We demonstrate that this pipeline can help reconstruct metabolic models that are similar to manually curated networks. Furthermore, the model is able to highlight previously overlooked pathways, thus advancing fundamental knowledge of non-model microbial systems or promoting their development toward biotechnological or environmental implementations.

Optimizing Metabolite Production with Neighborhood-Based Binary Quantum-Behaved Particle Swarm Optimization and Flux Balance Analysis.

Metabolic engineering is a rapidly evolving field that involves optimizing microbial cell factories to overproduce various industrial products. To achieve this, several tools, leveraging constraint-based stoichiometric models and metaheuristic algorithms like particle swarm optimization (PSO), have been developed. However, PSO can potentially get trapped in local optima. Quantum-behaved PSO (QPSO) overcomes this limitation, and our study further enhances its binary version (BQPSO) with a neighborhood topology, leading to the advanced neighborhood-based BQPSO (NBQPSO). Combined with flux balance analysis (FBA), this forms an innovative approach, NBQPSO-FBA, for identifying optimal knockout strategies to maximize the desired metabolite production. Additionally, we introduced a novel encoding strategy suitable for large-scale genome-scale metabolic models (GSMMs). Evaluated on four E. coli GSMMs (iJR904, iAF1260, iJO1366, and iML1515), NBQPSO-FBA matches or surpasses established bi-level linear programming (LP) and heuristic methods in metabolite production optimization. Notably, it achieved 90.69% realization of the theoretical maximum in acetate production and demonstrated comparable performance with leading algorithms in lactate production. The efficiency of NBQPSO-FBA, which requires fewer knockouts, makes it a practical and effective tool for optimizing microbial cell factories. This addresses the rising demand for microbial products across various industries.

Analyzing sorbitol biosynthesis using a metabolic network flux model of a lichenized strain of the green microalga Diplosphaera chodatii.

Diplosphaera chodatii, a unicellular terrestrial microalga found either free-living or in association with lichenized fungi, protects itself from desiccation by synthesizing and accumulating low-molecular-weight carbohydrates such as sorbitol. The metabolism of this algal species and the interplay of sorbitol biosynthesis with its growth, light absorption, and carbon dioxide fixation are poorly understood. Here, we used a recently available genome assembly for D. chodatii to develop a metabolic flux model and analyze the alga's metabolic capabilities, particularly, for sorbitol biosynthesis. The model contains 151 genes, 155 metabolites, and 194 unique metabolic reactions participating in 12 core metabolic pathways and five compartments. Both photoautotrophic and mixotrophic growths of D. chodatii were supported by the metabolic model. In the presence of glucose, mixotrophy led to higher biomass and sorbitol yields. Additionally, the model predicted increased starch biosynthesis at high light intensities during photoautotrophic growth, an indication that the "overflow hypothesis-stress-driven metabolic flux redistribution" could be applied to D. chodatii. Furthermore, the newly developed metabolic model of D. chodatii, iDco_core, captures both linear and cyclic electron flow schemes characterized in photosynthetic microorganisms and suggests a possible adaptation to fluctuating water availability during periods of desiccation. This work provides important new insights into the predicted metabolic capabilities of D. chodatii, including a potential biotechnological opportunity for industrial sorbitol biosynthesis.IMPORTANCELichenized green microalgae are vital components for the survival and growth of lichens in extreme environmental conditions. However, little is known about the metabolism and growth characteristics of these algae as individual microbes. This study aims to provide insights into some of the metabolic capabilities of Diplosphaera chodatii, a lichenized green microalgae, using a recently assembled and annotated genome of the alga. For that, a metabolic flux model was developed simulating the metabolism of this algal species and allowing for studying the algal growth, light absorption, and carbon dioxide fixation during both photoautotrophic and mixotrophic growth, in silico. An important capability of the new metabolic model of D. chodatii is capturing both linear and cyclic electron flow mechanisms characterized in several other microalgae. Moreover, the model predicts limits of the metabolic interplay between sorbitol biosynthesis and algal growth, which has potential applications in assisting the design of bio-based sorbitol production processes.

A GEOSPATIAL FRAMEWORK FOR SELF-SUSTAINING URBAN METABOLISM IN SOUTH TANJUNG DUREN AREA, WEST JAKARTA

Urban metabolism plays a crucial role in shaping the sustainable development of regions since it relates to how resources are consumed, transformed, and disposed of dynamically. In an increasingly urbanized world, effective regional planning is essential to address challenges such as resource depletion, environmental degradation, and socio-economic issues. This study aims to create an implementation framework for urban metabolism in South Tanjung Duren, West Jakarta, selected as part of a larger urban metabolism model. The research introduces a practical approach for urban designers, architects, and local authorities to establish self-sustaining urban areas. The methodology consists of two stages. In the first stage, the study thoroughly assesses the object of the study. This includes profiling the essential aspects of the object study, environmental impact assessments, mapping material and energy flows, and categorizing groups to optimize urban metabolism. Findings from this stage shape the second stage, which uses urban infill strategies via four approaches: Collecting Resources, Creating Biotopes, Channeling Energy, and Catalyzing. These strategies are simulated on the study site to analyze flows of goods, people, waste, biota, energy, food, water, sand, clay, and air, creating a geospatial framework for self-sustaining urban metabolism. The findings underscore that South Tanjung Duren has strong potential for implementing an urban metabolism framework, with simulations revealing increased resource efficiency, effective waste reduction, improved green space, and minimized environmental impact. This framework not only enhances resource optimization and environmental protection but also fosters sustainable development, positioning South Tanjung Duren as a replicable model for resilient, self-sustaining urban neighborhoods.

Dissecting metabolic landscape of alveolar macrophage

The highly plastic nature of Alveolar Macrophage (AM) plays a crucial role in the defense against inhaled particulates and pathogens in the lungs. Depending on the signal, AM acquires either the classically activated M1 phenotype or the alternatively activated M2 phenotype. In this study, we investigate the metabolic shift in the activated phases of AM (M1 and M2 phases) by reconstructing context specific Genome-Scale Metabolic (GSM) models. Metabolic pathways such as pyruvate metabolism, arachidonic acid metabolism, chondroitin/heparan sulfate biosynthesis, and heparan sulfate degradation are found to be important driving forces in the development of the M1/M2 phenotypes. Additionally, we formulated a bilevel optimization framework named MetaShiftOptimizer to identify minimal modifications that shift one activated state (M1/M2) to the other. The identified reactions involve metabolites such as glycogenin, L-carnitine, 5-hydroperoxy eicosatetraenoic acid, and leukotriene B4, which show potential to be further investigated as significant factors for developing efficient therapy targets for severe respiratory disorders in the future. Overall, our study contributes to the understanding of the metabolic capabilities of the M1 and M2 phenotype of AM and identifies pathways and reactions that can be potential targets for polarization shift and also be used as therapeutic strategies against respiratory diseases.

Dasatinib and Quercetin as Senolytic Drugs Improve Fat Deposition and Exhibit Antifibrotic Effects in the Medaka Metabolic Dysfunction-Associated Steatotic Liver Disease Model

Metabolic dysfunction-associated steatotic liver disease (MASLD) causes cellular senescence due to oxidative stress, endoplasmic reticulum stress, and ectopic fat deposition in the liver. Recently, dasatinib, an antitumor agent, and quercetin, a dietary supplement, were combined as a senolytic drug to eliminate senescent cells. Thus, this study aimed to examine the effects of dasatinib and quercetin administration on removing senescent cells and their therapeutic effects on MASLD in a medaka MASLD model. Dasatinib and quercetin were administered to a medaka MASLD model, which was fed a high-fat diet by dissolving them in aquarium water. The results revealed that senescent cells in the liver were increased in the HFD group but improved in the treatment group. Hematoxylin and eosin staining also showed that treatment improved fat deposition in hepatocytes. In addition, TGFβ1, a driver factor of fibrosis, was reduced in the treatment group. Dasatinib and quercetin eliminated senescent cells in MASLD, attenuated fat deposition, and suppressed fibrosis gene expression. The results indicate that dasatinib and quercetin as senolytic drugs are novel therapeutic agents that reduce MASLD.

Heterologous Biosynthesis of Taxifolin in Yarrowia lipolytica: Metabolic Engineering and Genome-Scale Metabolic Modeling.

Taxifolin, also known as dihydroquercetin (DHQ), is a flavonoid recognized for its potent antioxidant properties and a wide range of biological activities, including anti-tumor, antiviral, and immunomodulatory effects. Conventional extraction and chemical synthesis methods for taxifolin are often limited by low yields and associated environmental concerns. In this study, we investigated the heterologous biosynthesis of taxifolin in Yarrowia lipolytica through a combination of metabolic engineering and genome-scale metabolic modeling (GSM), complemented by flux balance analysis (FBA). We engineered Yarrowia lipolytica by introducing key biosynthetic genes and successfully synthesized taxifolin using naringenin (NAR) as a substrate, chosen for its low cost. Fermentation experiments demonstrated an optimal taxifolin yield of 10% at a substrate concentration of 200mg/L naringenin, with a maximum yield of 26.4mg/L taxifolin at 1g/L naringenin. To further enhance production, we applied a marker-free Cre-loxP-based gene integration method, allowing stable genomic integration of key genes, which increased taxifolin yield to 34.9mg/L at 1g/L naringenin. Additionally, intermediate metabolites eriodictyol (ERI) and dihydrokaempferol (DHK) accumulated to concentrations of 89.2mg/L and 21.7mg/L, respectively. Furthermore, we integrated metabolic data into a GSM and applied FBA to optimize the taxifolin biosynthetic pathway. Through Pareto frontier analysis, sensitivity analysis, flux variability analysis, and single gene deletion simulations, we identified key genetic modifications that significantly enhanced taxifolin yield. Overexpression of GND1 and IDP2 increased yields by 94% and 155%, respectively, while knockout of LIP2 led to a 46% increase. Using tri-baffled shake flasks to improve oxygen supply resulted in a 120% yield increase, whereas YPG medium decreased yield by 59%, validating our model's accuracy. To ensure stable and efficient gene expression, we integrated multi-copy constructs into the ribosomal DNA (rDNA) locus of Yarrowia lipolytica, doubling taxifolin production. These results demonstrate the effectiveness of GSM and FBA in addressing bottlenecks in microbial taxifolin biosynthesis and provide a basis for future optimization and large-scale production.

Optimal control of glucose-insulin dynamics via delay differential model with fractional-order

This paper proposes a delay differential model with fractional order for glucose-insulin endocrine, metabolic regulation model, incorporating beta-cell dynamics to regulate and maintain bloodstream insulin concentration. In the model, two time delays are involved, namely δg and δι, which represent delayed insulin secretion and delayed glucose reduction. A moderate hyperglycemia results in beta-cell growth (negative feedback), while a severe hyperglycemia results in beta-cell reduction (positive feedback). When a time delay passes a bifurcation point, Hopf bifurcation occurs. It is evident from biological findings that the model exhibits periodic oscillations. Furthermore, we present an optimal control problem for external insulin infusions to minimize prolonged high blood sugar levels. Numerical simulations have validated the theoretical results.

An improved dynamic metabolic model for application to biota

Any major nuclear facility must ensure the conservation of biodiversity regarding radiation protection of biota. A special concern is for tritium (3H) and radiocarbon (14C) transfer in wild mammals, birds and reptiles. Hydrogen and carbon are the main components of biological tissues and enter the life cycle. The present study improves the scientific bases of a previous model, analyses the uncertainty of input parameters and tests the model for a larger range of mammals and birds. The biological and metabolic half-times for organically bound tritium (OBT) and 14C are linked with energy metabolism and recent results are revised in relation with metabolic scaling. A large data base regarding basal metabolic rate (BMR), field metabolic rate (FMR), and organ mass is used for input information of the present model, which considers brain as a separate compartment. Metabolic energy partition in organs of active animal is defined and the factors affecting the metabolic rate are analysed. Body and ambient temperature, diet and habitat, and phylogeny are important factors considered in animal adaptation to environment. The available experimental data for carbon turnover rates in animals are analysed and it is observed that the experimental conditions are not appropriate for wild animals. The link between 13,14C and 134,137Cs turnover rate is analysed and the present metabolic approach is successfully tested for mammals and reptiles. Considering animal adaptation and the large data base for 134,137Cs, the radiological impact of accidental releases of 3H and 14C on biota can be pursued in the future research.

Tailored impact of dietary fibers on gut microbiota: a multi-omics comparison on the lean and obese microbial communities

BackgroundPrevious studies have shown that microbial communities differ in obese and lean individuals, and dietary fiber can help reduce obesity-related conditions through diet-gut microbiota interactions. However, the mechanisms by which dietary fibers shape the gut microbiota still need to be elucidated. In this in vitro study, we examined how apple fibers affect lean and obese microbial communities on a global scale. We employed a high-throughput micro-matrix bioreactor system and a multi-omics approach to identify the key microorganisms and metabolites involved in this process.ResultsInitially, metagenomics and metabolomics data indicated that obese and lean microbial communities had distinct starting microbial communities. We found that obese microbial community had different characteristics, including higher levels of Ruminococcus bromii and lower levels of Faecalibacterium prausnitzii, along with an increased Firmicutes:Bacteroides ratio. Afterward, we exposed obese and lean microbial communities to an apple as a representative complex food matrix, apple pectin as a soluble fiber, and cellulose as an insoluble fiber. Dietary fibers, particularly apple pectin, reduced Acidaminococcus intestini and boosted Megasphaera and Akkermansia in the obese microbial community. Additionally, these fibers altered the production of metabolites, increasing beneficial indole microbial metabolites. Our results underscored the ability of apple and apple pectin to shape the obese gut microbiota.ConclusionWe found that the obese microbial community had higher branched-chain amino acid catabolism and hexanoic acid production, potentially impacting energy balance. Apple dietary fibers, especially pectin, influenced the obese microbial community, altering both species and metabolites. Notably, the apple pectin feeding condition affected species like Klebsiella pneumoniae and Bifidobacterium longum. By using genome-scale metabolic modeling, we discovered a mutualistic cross-feeding relationship between Megasphaera sp. MJR8396C and Bifidobacterium adolescentis. This in vitro study suggests that incorporating apple fibers into the diets of obese individuals can help modify the composition of gut bacteria and improve metabolic health. This personalized approach could help mitigate the effects of obesity.1sSYKieb1FyPG4CFVZT5APVideo Graphical

Paradigm of engineering recalcitrant non-model microorganism with dominant metabolic pathway as a biorefinery chassis

The development and implementation of microbial chassis cells have profound impacts on circular economy. Non-model bacterium Zymomonas mobilis is an excellent chassis owing to its extraordinary industrial characteristics. Here, the genome-scale metabolic model iZM516 is improved and updated by integrating enzyme constraints to simulate the dynamics of flux distribution and guide pathway design. We show that the innate dominant ethanol pathway of Z. mobilis restricts the titer and rate of these biochemicals. A dominant-metabolism compromised intermediate-chassis (DMCI) strategy is then developed through introducing low toxicity but cofactor imbalanced 2,3-butanediol pathway, and a recombinant D-lactate producer is constructed to produce more than 140.92 g/L and 104.6 g/L D-lactate (yield > 0.97 g/g) from glucose and corncob residue hydrolysate, respectively. Additionally, techno-economic analysis (TEA) and life cycle assessment (LCA) demonstrate the commercialization feasibility and greenhouse gas reduction capability of lignocellulosic D-lactate. This work thus establishes a paradigm for engineering recalcitrant microorganisms as biorefinery chassis.

Applications of marine microbial community models in the nature-based economy

Marine microorganisms are central to global ecological and biogeochemical systems, with their intricate interactions shaping community dynamics. While meta-omics data sets have revolutionized marine microbial ecology, they often provide fragmented insights, underscoring the need for advanced integrative modeling frameworks. In this review, we highlight the potential that community genome-scale metabolic models (cGEMs), in combination with meta-omics and environmental data sets, offer in advancing marine microbial ecology. We explore 3 key applications: quantifying marine ecosystem services, guiding bioremediation strategies for environmental challenges, and enhancing climate and biogeochemical models. Furthermore, we propose novel indices derived from cGEMs to assess microbial contributions to ecosystem functions, potentially informing economic valuation strategies for marine conservation. This interdisciplinary approach paves the way for innovative strategies in biotechnology, environmental restoration, and the development of nature-aligned economic systems, ultimately contributing to the preservation and sustainable use of marine ecosystems.

Theoretical basis for cell deaths

Understanding deaths and life-death boundaries of cells is a fundamental challenge in biological sciences. In this study, we present a theoretical framework for investigating cell death. We conceptualize cell death as a controllability problem within dynamical systems, and compute the life-death boundary through the development of “stoichiometric rays.” This method utilizes enzyme activity as control parameters, exploiting the inherent property of enzymes to enhance reaction rates without affecting thermodynamic potentials. This approach facilitates the efficient evaluation of the global controllability of models. We demonstrate the utility of our framework using its application to a toy metabolic model, where we delineate the life-death boundary. The formulation of cell death through mathematical principles provides a foundation for the theoretical study of cellular mortality. Published by the American Physical Society 2024

Efek Ekstrak Arbei (Rubus idaeus) terhadap Kadar Kolesterol Total Tikus Wistar Model Sindrom Metabolik

Metabolic syndrome is characterized by low HDL levels, high triglycerides, hypertension, increased blood glucose, and obesity, often associated with obesity and type 2 diabetes. This study aimed to investigate the effect of Wild Red Raspberry (Rubus idaeus) on total cholesterol levels in Wistar rats (Rattus norvegicus) with a metabolic syndrome model. Method This research employed a quantitative experimental laboratory study with a pretest-posttest control group design. A total of 30 male Wistar rats were divided into five groups: K1 (negative control), K2 (positive control), and K3, K4, K5 (metabolic syndrome) treated with Rubus idaeus extract at doses of 30 mg/200gBW/day, 60 mg/200gBW/day, and 90 mg/200gBW/day. Total cholesterol levels were measured on days 8, 36, and 64. Data analysis was performed using Shapiro-Wilk normality test, and the results were not normal in distribution and hemogenity although after transformatted, so non-parametric tests Kruskal-Wallis, were used, followed by Mann-Whitney tests. Result ,the administration of Rubus idaeus extract resulted in the highest total cholesterol levels in group K3 and the lowest in group K5. Statistical analysis showed significant differences (p < 0.05) before and after treatment in each group, with Pearson correlation tests demonstrating a very strong relationship between the dosage of Rubus idaeus and total cholesterol levels. Conclusion, the administration of Rubus idaeus extract significantly reduces total cholesterol levels, with the dose of 90 mg/200gBW being the most effective in lowering total cholesterol levels.

A review and prospects: Multi-omics and artificial intelligence-based approaches to understanding the effects of lactic acid bacteria and yeast interactions on fermented foods

There are numerous reports on the effect of co-culturing lactic acid bacteria (LAB) and yeast on the quality of fermented foods. The interactions between LAB and yeast affect the flavour, texture and even safety of fermented foods. Examining the specific mechanisms of these interactions becomes essential, but existing research methods are insufficient to meet the growing demands of scientific research. Multi-omics analysis provides more comprehensive information on LAB and yeast interactions than the widely used single-omics. Additionally, although the use of bioinformatics tools such as ModelSEED and CarveMe has simplified the construction of metabolic network models, there are still shortcomings in terms of accuracy and convenience. There is an urgent need to explore new methods to accelerate the study of microbial interaction mechanisms. This review summarizes the interaction between LAB and yeast in fermented foods, as well as the current research progress in revealing their interaction using multi-omics and bioinformatics tools. In the future, artificial intelligence will be a powerful aid in constructing metabolic models and obtaining models conveniently. Elucidation of interaction mechanisms at the spatial level will become more common, and more comprehensive research results will lead to further improvements in industrial practice.

Carbon translocation within land snails affects the carbon isotopic fractionation

Stable carbon isotope composition (δ13C) of land snail materials is widely used as a proxy for reconstructing past vegetation and environmental changes. Interpretation δ13C data is challenging due to the complexities of snail physiology. This study cultured Lissachatina fulica (Bowdich, 1822) snails to investigate δ13C of their soft tissues and excrement under various plant types and dietary carbonate content. Our results show that snails primarily assimilate organic matter (OM), with limited use of dietary carbonates. δ13C of organic matter in excrement correlates positively with that in the diet, but exhibits deviations due to variable carbon isotopic fractionation during assimilation. Tissue δ13C (δ13Ctissue) is positively related to the δ13C of OM (δ13COM) in the diet, varying by turnover rates, with faster turnover rate, such as the digestive gland, exhibiting more negative δ13Ctissue values. Moreover, carbon isotopic fractionation between tissues and diet (Δ13Ctissue-OM) differs based on plant type. Snails fed with C3 plants show positive Δ13Ctissue-OM values, whereas those fed with C4 plants exhibit negative values. These Δ13Ctissue-OM values are far from equilibrium (Δ13Ctissue-OM = 0) with conditions of rapid snail growth. Our findings indicate that the snails fed with C3 and C4 plants result in distinct carbon isotopic fractionation, leading to the deviation in δ13C between plants and land snail materials (e.g., excrement, tissues, shells and shell-bound organic matter). A comprehensive model of carbon metabolism and isotopic fractionation in snails is proposed including the assimilation process from diet to soft tissues and excretion. This study highlights the importance of physiological factors such as growth rates and turnover rates when interpreting δ13C of land snail materials for paleoenvironmental reconstructions.

Identification and characterization of etomidate and metomidate metabolites in zebrafish, HLMs and S9 fraction by quadrupole-orbitrap LC-MS/MS for drug control

Etomidate is a common non-barbiturate anesthetic with psychoactivity, and metomidate, a structural modifier of etomidate, also has the same psychoactive effect, and the abuse of both has gradually intensified. In this study, etomidate, metomidate and their metabolic profiles with 4 days postfertilization (4dpf) zebrafish, 4 months postfertilization (4mpf) zebrafish, human liver microsomes (HLMs) and human liver S9 fraction were investigated using Liquid chromatography with high resolution mass spectrometry (LC-HRMS) for the first time. 14 etomidate metabolites and 11 metomidate metabolites were found, and the related metabolic pathways included monohydroxylation, dihydroxylation, dehydrogenation, N-dealkylation, O-dealkylation, oxidation, N-glucuronidation, O-glucuronidation and combination. Etomidate acid (E6 and M6) was considered a common biomarker for monitoring the abuse of etomidate and its analogues. Two characteristic metabolites (E4 and M4) could be used as biomarkers to monitor etomidate or metomidate abuse, respectively. Dealkylation, hydroxylation and glucuronidation were the main metabolic pathways of etomidate and metomidate. The differences in metabolic profiles of the three metabolic models were also compared for the first time. The results of this study can provide important reference for the detection of target compounds against the abuse of etomidate and metomidate, and the metabolic analysis of similar substances.

Wild-Type Domestication: Loss of Intrinsic Metabolic Traits Concealed by Culture in Rich Media

Bacteria are typically isolated on rich media to maximise isolation success, removing them from their native evolutionary context. This eliminates selection pressures, enabling otherwise deleterious genomic events to accumulate. Here, we present a cautionary tale of these ‘quiet mutations’ which can persist unnoticed in bacterial culture lines. We used a combination of microbiological culture (standard and minimal media conditions), whole genome sequencing and metabolic modelling to investigate putative Klebsiella pneumoniae L-histidine auxotrophs. Additionally, we used genome-scale metabolic modelling to predict auxotrophies among completed public genomes (n = 2637). Two sub-populations were identified within a K. pneumoniae frozen stock, differing in their ability to grow in the absence of L-histidine. These sub-populations were the same ‘strain’, separated by eight single nucleotide variants and an insertion sequence-mediated deletion of the L-histidine biosynthetic operon. The His− sub-population remained undetected for > 10 years despite its inclusion in independent laboratory experiments. Genome-scale metabolic models predicted 0.8% public genomes contained ≥ 1 auxotrophy, with purine/pyrimidine biosynthesis and amino acid metabolism most frequently implicated. We provide a definitive example of the role of standard rich media culture conditions in obscuring biologically relevant mutations (i.e. nutrient auxotrophies) and estimate the prevalence of such auxotrophies using public genome collections. While the prevalence is low, it is not insignificant given the thousands of K. pneumoniae that are isolated for global surveillance and research studies each year. Our data serve as a pertinent reminder that rich-media culturing can cause unnoticed wild-type domestication.

A Study of the Community Relationships Between Methanotrophs and Their Satellites Using Constraint-Based Modeling Approach.

Biotechnology continues to drive innovation in the production of pharmaceuticals, biofuels, and other valuable compounds, leveraging the power of microbial systems for enhanced yield and sustainability. Genome-scale metabolic (GSM) modeling has become an essential approach in this field, which enables a guide for targeting genetic modifications and the optimization of metabolic pathways for various industrial applications. While single-species GSM models have traditionally been employed to optimize strains like Escherichia coli and Lactococcus lactis, the integration of these models into community-based approaches is gaining momentum. Herein, we present a pipeline for community metabolic modeling with a user-friendly GUI, applying it to analyze interactions between Methylococcus capsulatus, a biotechnologically important methanotroph, and Escherichia coli W3110 under oxygen- and nitrogen-limited conditions. We constructed models with unmodified and homoserine-producing E. coli strains using the pipeline implemented in the original BioUML platform. The E. coli strain primarily utilized acetate from M. capsulatus under oxygen limitation. However, homoserine produced by E. coli significantly reduced acetate secretion and the community growth rate. This homoserine was taken up by M. capsulatus, converted to threonine, and further exchanged as amino acids. In nitrogen-limited modeling conditions, nitrate and ammonium exchanges supported the nitrogen needs, while carbon metabolism shifted to fumarate and malate, enhancing E. coli TCA cycle activity in both cases, with and without modifications. The presence of homoserine altered cross-feeding dynamics, boosting amino acid exchanges and increasing pyruvate availability for M. capsulatus. These findings suggest that homoserine production by E. coli optimizes resource use and has potential for enhancing microbial consortia productivity.

Enzyme-constrained Metabolic Model of Treponema pallidum Identified Glycerol-3-phosphate Dehydrogenase as an Alternate Electron Sink.

This study advances our understanding of Treponema pallidum , the syphilis-causing pathogen, through the reconstruction of iTP251, the first genome-scale metabolic model for this organism, and its enzyme-constrained version, ec-iTP251. The work addresses challenges of studying T. pallidum due to its strict intracellular nature and difficulties in in vitro cultivation. Validated with strong agreement to proteomics data, the model demonstrates high predictive reliability. Key insights include unique metabolic adaptations such as lactate uptake for ATP production and alternative redox-balancing mechanisms. These findings provide a robust framework for future studies aimed at unraveling the pathogen's survival strategies and identifying potential metabolic vulnerabilities.