Metabolic Model Research Articles

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.

Disentangling the microbial genomic traits associated with aromatic hydrocarbon degradation in a jet fuel-contaminated aquifer.

Spills of petroleum or its derivatives in the environment lead to an enrichment of microorganisms able to degrade such compounds. The interactions taking place in such microbial communities are complex and poorly understood, since they depend on multiple factors, including diversity and metabolic potential of the microorganisms and a broad range of fluctuating environmental conditions. In our previous study, a complete characterization, based on high-throughput sequencing, was performed in a jet-fuel plume using soil samples and in in-situ microcosms amended with hydrocarbons and exposed for 120days. Herein, we propose a metabolic model to describe the monoaromatic hydrocarbon degradation process that takes place in such jet-fuel-contaminated sites, by combining genome-centered analysis, functional predictions, and flux balance analysis (FBA). In total, twenty high/medium quality MAGs were recovered; three of them assigned to anaerobic bacteria (Thermincolales, Geobacter and Pelotomaculaceace) and one affiliated to the aerobic bacterium Acinetobacter radioresistens, potentially the main players of hydrocarbon degradation in jet-fuel plumes. Taxonomic assignment of the genes indicated that a putative new species of Geobacteria has the potential for anaerobic degradation pathway, while the Pelotomaculaceae and Thermincolales members probably act via syntrophy oxidizing acetate and hydrogen (fermentation products of oil degradation) via sulfate and/or nitrate reduction.

Sensitivities in protein allocation models reveal distribution of metabolic capacity and flux control.

Expanding on constraint-based metabolic models, protein allocation models (PAMs) enhance flux predictions by accounting for protein resource allocation in cellular metabolism. Yet, to this date, there are no dedicated methods for analyzing and understanding the growth-limiting factors in simulated phenotypes in PAMs. Here, we introduce a systematic framework for identifying the most sensitive enzyme concentrations (sEnz) in PAMs. The framework exploits the primal and dual formulations of these models to derive sensitivity coefficients based on relations between variables, constraints, and the objective function. This approach enhances our understanding of the growth-limiting factors of metabolic phenotypes under specific environmental or genetic conditions. Compared to other traditional methods for calculating sensitivities, sEnz requires substantially less computation time and facilitates more intuitive comparison and analysis of sensitivities. The sensitivities calculated by sEnz cover enzymes, reactions and protein sectors, enabling a holistic overview of the factors influencing metabolism. When applied to an Escherichia coli PAM, sEnz revealed major pathways and enzymes driving overflow metabolism. Overall, sEnz offers a computational efficient framework for understanding PAM predictions and unravelling the factors governing a particular metabolic phenotype. sEnz is implemented in the modular toolbox for the generation and analysis of PAMs in Python (PAModelpy; v.0.0.3.3), available on Pypi (https://pypi.org/project/PAModelpy/). The source code together with all other python scripts and notebooks are available on GitHub (https://github.com/iAMB-RWTH-Aachen/PAModelpy). Supplementary data are available at Bioinformatics online.

Strategy for Optimizing Vitamin B12 Production in Pseudomonas putida KT2440 Using Metabolic Modeling.

Background/Objectives: Vitamin B12 is very important for human health, as it is a cofactor for enzymatic activities and plays various roles in human physiology. It is highly valued in the pharmaceutical, food, and additive production industries. Some of the bacteria currently used for the vitamin production are difficult to modify with gene-editing tools and may have slow growth. We propose the use of the bacteria Pseudomonas putida KT2440 for the production of vitamin B12 because it has a robust chassis for genetic modifications. The present wok evaluates P. putida KT2440 as a host for vitamin B12 production and explore potential gene-editing optimization strategies. Methods: We curated and modified a genome-scale metabolic model of Pseudomonas putida KT2440 and evaluated different strategies to optimize vitamin B12 production using the knockin and OptGene algorithms from the COBRA Toolbox. Furthermore, we examined the presence of riboswitches as cis-regulatory elements and calculated theoretical biomass growth yields and vitamin B12 production using a flux balance analysis (FBA). Results: According to the flux balance analysis of P. putida KT2440 under culture conditions, the biomass production values could reach 1.802 gDW-1·h1·L-1, and vitamin B12 production could reach 0.359 µmol·gDW-1·h-1·L-1. The theoretical vitamin B12 synthesis rate calculated using P. putida KT2040 with two additional reactions was 14 times higher than that calculated using the control, Pseudomonas denitrificans, which has been used for the industrial production of this vitamin. Conclusions: We propose that, with the addition of aminopropanol linker genes and the modification of riboswitches, P. putida KT2440 may become a suitable host for the industrial production of vitamin B12.

Metabolic Flux Analysis of Xanthomonas oryzae Treated with Bismerthiazol Revealed Glutathione Oxidoreductase in Glutathione Metabolism Serves as an Effective Target.

Bacterial blight (BB) of rice caused by Xanthomonas oryzae pathovar oryzae (Xoo) is a serious global rice disease. Due to increasing bactericide resistance, developing new inhibitors is urgent. Drug repositioning offers a potential strategy to address this issue. In this study, we integrated transcriptional data into a genome-scale metabolic model (GSMM) to screen novel anti-Xoo targets. Two RNA-seq datasets (before and after bismerthiazol treatment) were used to constrain the GSMM and simulate metabolic processes. Metabolic fluxes were calculated using parsimonious flux balance analysis (pFBA) identifying reactions with significant changes for target screening. Glutathione oxidoreductase (GSR) was selected as a potential anti-Xoo target and validated through antibacterial experiments. Virtual screening based on the target identified DB12411 as a lead compound with the potential for new antibacterial agents. This approach demonstrates that integrating metabolic networks and transcriptional data can aid in both understanding antibacterial mechanisms and discovering novel drug targets.

The role of low-carbohydrate, high-fat diet in modulating autophagy and endoplasmic reticulum stress in aortic endothelial dysfunction of metabolic syndrome animal model.

Endothelial dysfunction (ED) is induced by insulin resistance, mediated by endoplasmic reticulum (ER) stress and disturbed autophagy. This study investigates the protective role of a low-carbohydrate, high-fat (LCHF) diet on ED, ER stress, and autophagy dysregulation in an experimental animal model of metabolic syndrome. Forty male Sprague-Dawley rats were divided into four groups: a Control group (standard diet) and three Dexamethasone (DEX) treated groups. Group II continued the standard diet, Group III received an LCHF diet, and Group IV received a high-carbohydrate, low-fat (HCLF) diet. At the end of the experiment, aortic tissue samples were obtained and used for histological, immunohistochemical (Endothelin and PCNA, biochemical MDA, TCA, NO, 8-OH-dG, and Nrf2/ARE protein) and molecular (Endothelin, eNOS, Nrf-2 α, p62, LC3, BECN-1, PINK1, CHOP, BNIP3, PCNA) analysis. Oxidative stress, autophagy markers, and ED markers are increased in the metabolic syndrome group. LCHF diet mitigates the adverse effects of DEX on endothelial dysfunction and oxidative stress, as evidenced by reduced BMI, HOMA-IR, and improved histological and molecular parameters. Oxidative stress, autophagy dysregulation, and ER stress play crucial roles in the pathogenesis of insulin resistance-induced endothelial dysfunction. An LCHF diet offers protective benefits against insulin resistance and related comorbidities, including endothelial dysfunction.

Dynamic metabolic modeling of ATP allocation during viral infection.

Viral pathogens, like SARS-CoV-2, hijack the host's macromolecular production machinery, imposing an energetic burden that is distributed across cellular metabolism. To explore the dynamic metabolic tension between the host's survival and viral replication, we developed a computational framework that uses genome-scale models to perform dynamic Flux Balance Analysis of human cell metabolism during virus infections. Relative to previous models, our framework addresses the physiology of viral infections of non-proliferating host cells through two new features. First, by incorporating the lipid content of SARS-CoV-2 biomass, we discovered activation of previously overlooked pathways giving rise to new predictions of possible drug targets. Furthermore, we introduce a dynamic model that simulates the partitioning of resources between the virus and the host cell, capturing the extent to which the competition depletes the human cells from essential ATP. By incorporating viral dynamics into our COMETS framework for spatio-temporal modeling of metabolism, we provide a mechanistic, dynamic and generalizable starting point for bridging systems biology modeling with viral pathogenesis. This framework could be extended to broadly incorporate phage dynamics in microbial systems and ecosystems.