Search Results (219)

Per- and polyfluoroalkyl substances (PFASs) are widespread in the environment, bioaccumulate in humans, and lead to disease and organ injury, such as liver steatosis. However, we lack a clear understanding of how these chemicals cause organ-level toxicity. Here, we aimed to analyze PFAS-induced metabolic perturbations in male and female rat livers by combining a genome-scale metabolic model (GEM) and toxicogenomics. The combined approach overcomes the limitations of the individual methods by taking into account the interaction between multiple genes for metabolic reactions and using gene expression to constrain the predicted mechanistic possibilities. We obtained transcriptomic data from an acute exposure study, where male and female rats received a daily PFAS dose for five consecutive days, followed by liver transcriptome measurement. We integrated the transcriptome expression data with a rat GEM to computationally predict the metabolic activity in each rat’s liver, compare it between the control and PFAS-exposed rats, and predict the benchmark dose (BMD) at which each chemical induced metabolic changes. Overall, our results suggest that PFAS-induced metabolic changes occurred primarily within the lipid and amino acid pathways and were similar between the sexes but varied in the extent of change per dose based on sex and PFAS type. Specifically, we identified that PFASs affect fatty acid-related pathways (biosynthesis, oxidation, and sphingolipid metabolism), energy metabolism, protein metabolism, and inflammatory and inositol metabolite pools, which have been associated with fatty liver and/or insulin resistance. Based on these results, we hypothesize that PFAS exposure induces changes in liver metabolism and makes the organ sensitive to metabolic diseases in both sexes. Furthermore, we conclude that male rats are more sensitive to PFAS-induced metabolic aberrations in the liver than female rats. This combined approach using GEM-based predictions and BMD analysis can help develop mechanistic hypotheses regarding how toxicant exposure leads to metabolic disruptions and how these effects may differ between the sexes, thereby assisting in the metabolic risk assessment of toxicants. Full article

Recent advances in computational biology have provided powerful tools for analyzing, modeling, and optimizing probiotic microorganisms, thereby supporting their development as promising agents for improving human health. The essential role of the microbiota in regulating physiological processes and preventing disease has driven interest in the rational design of next-generation probiotics. This review highlights progress in in silico approaches for enhancing the functionality of probiotic strains. Particular attention is given to genome-scale metabolic models, advanced simulation algorithms, and AI-driven tools that provide deeper insight into microbial metabolism and enable precise probiotic optimization. The integration of these methods with multi-omics data has greatly improved our ability to predict strain behavior and design probiotics with specific health benefits. Special focus is placed on modeling probiotic–prebiotic interactions and host–microbiome dynamics, which are essential for the development of functional food products. Despite these achievements, key challenges remain, including limited model accuracy, difficulties in simulating complex host–microbe systems, and the absence of unified standards for validating in silico-optimized strains. Addressing these gaps requires the development of integrative modeling platforms and clear regulatory frameworks. This review provides a critical overview of current advances, identifies existing barriers, and outlines future directions for the application of computational strategies in probiotic research. Full article

Pancreatic cancer is frequently accompanied by cancer-associated cachexia, a debilitating metabolic syndrome marked by progressive skeletal muscle wasting and systemic metabolic dysfunction. This study presents a systems biology framework to simultaneously identify therapeutic targets for both pancreatic ductal adenocarcinoma (PDAC) and its associated cachexia (PDAC-CX), using cell-specific genome-scale metabolic models (GSMMs). The human metabolic network Recon3D was extended to include protein synthesis, degradation, and recycling pathways for key inflammatory and structural proteins. These enhancements enabled the reconstruction of cell-specific GSMMs for PDAC and PDAC-CX, and their respective healthy counterparts, based on transcriptomic datasets. Medium-independent metabolic biomarkers were identified through Parsimonious Metabolite Flow Variability Analysis and differential expression analysis across five nutritional conditions. A fuzzy multi-objective optimization framework was employed within the anticancer target discovery platform to evaluate cell viability and metabolic deviation as dual criteria for assessing therapeutic efficacy and potential side effects. While single-enzyme targets were found to be context-specific and medium-dependent, eight combinatorial targets demonstrated robust, medium-independent effects in both PDAC and PDAC-CX cells. These include the knockout of SLC29A2, SGMS1, CRLS1, and the RNF20–RNF40 complex, alongside upregulation of CERK and PIKFYVE. The proposed integrative strategy offers novel therapeutic avenues that address both tumor progression and cancer-associated cachexia, with improved specificity and reduced off-target effects, thereby contributing to translational oncology. Full article

Background: Exposure to per- and polyfluoroalkyl substances (PFAS, including 7H-Perfluoro-4-methyl-3,6-dioxaoctanesulfonic acid (PFESA-BP2), perfluorooctanoic acid (PFOA), and hexafluoropropylene oxide (GenX), has been associated with liver dysfunction. While previous research has characterized PFAS-induced hepatic lipid alterations, their downstream effects on energy metabolism remain unclear. This study investigates metabolic alterations in the liver following PFAS exposure to identify mechanisms leading to hepatoxicity. Methods: We analyzed RNA sequencing datasets of mouse liver tissues exposed to PFAS to identify metabolic pathways influenced by the chemical toxicant. We integrated the transcriptome data with a mouse genome-scale metabolic model to perform in silico flux analysis and investigated reactions and genes associated with lipid and energy metabolism. Results: PFESA-BP2 exposure caused dose- and sex-dependent changes, including upregulation of fatty acid metabolism, β-oxidation, and cholesterol biosynthesis. On the contrary, triglycerides, sphingolipids, and glycerophospholipids metabolism were suppressed. Simulations from the integrated genome-scale metabolic models confirmed increased flux for mevalonate and lanosterol metabolism, supporting potential cholesterol accumulation. GenX and PFOA triggered strong PPARα-dependent responses, especially in β-oxidation and lipolysis, which were attenuated in PPARα^−/− mice. Mitochondrial fatty acid transport and acylcarnitine turnover were also disrupted, suggesting impaired mitochondrial dysfunction. Additional PFAS effects included perturbations in the tricarboxylic acid (TCA) cycle, oxidative phosphorylation, and blood–brain barrier (BBB) function, pointing to broader systemic toxicity. Conclusions: Our findings highlight key metabolic signatures and suggest PFAS-mediated disruption of hepatic and possibly neurological functions. This study underscores the utility of genome-scale metabolic modeling as a powerful tool to interpret transcriptomic data and predict systemic metabolic outcomes of toxicant exposure. Full article

Vibrio parahaemolyticus is a pathogenic bacterium widely distributed in marine environments, posing significant threats to aquatic organisms and human health. The overuse and misuse of antibiotics has led to the development of multidrug- and pan-resistant V. parahaemolyticus strains. There is an urgent need for novel antibacterial therapies with innovative mechanisms of action. In this work, a genome-scale metabolic network model (GMSN) of V. parahaemolyticus, named VPA2061, was reconstructed to predict the metabolites that can be explored as potential drug targets for eliminating V. parahaemolyticus infections. The model comprises 2061 reactions and 1812 metabolites. Through essential metabolite analysis and pathogen–host association screening with VPA2061, 10 essential metabolites critical for the survival of V. parahaemolyticus were identified, which may serve as key candidates for developing new antimicrobial strategies. Additionally, 39 structural analogs were found for these essential metabolites. The molecular docking analysis of the essential metabolites and structural analogs further investigated the potential value of these metabolites for drug design. The GSMN reconstructed in this work provides a new tool for understanding the pathogenic mechanisms of V. parahaemolyticus. Furthermore, the analysis results regarding the essential metabolites hold profound implications for the development of novel antibacterial therapies for V. parahaemolyticus-related disease. Full article

Cordycepin, a bioactive adenosine analog, holds promise in pharmaceutical and health product development. However, large-scale production remains constrained by the limitations of natural producers, Cordyceps spp. Herein, we report the reconstruction of the first genome-scale metabolic model (GSMM) for a cordycepin-producing strain of recombinant Aspergillus oryzae. The model, iNR1684, incorporated 1684 genes and 1947 reactions with 93% gene-protein-reaction coverage, which was validated by the experimental biomass composition and growth rate. In silico analyses identified key gene amplification targets in the pentose phosphate and one-carbon metabolism pathways, indicating that folate metabolism is crucial for enhancing cordycepin production. Nutrient optimization simulations revealed that chitosan, D-glucosamine, and L-aspartate preferentially supported cordycepin biosynthesis. Additionally, a carbon-to-nitrogen ratio of 11.6:1 was identified and experimentally validated to maximize production, higher than that reported for Cordyceps militaris. These findings correspond to a faster growth rate, enhanced carbon assimilation, and broader substrate utilization by A. oryzae. This study demonstrates the significant role of GSMM in uncovering rational engineering strategies and provides a quantitative framework for precision fermentation, offering scalable and sustainable solutions for industrial cordycepin production. Full article

Food fermentation is driven by microbial interactions. This article reviews the types of microbial interactions during food fermentation, the research strategies employed, and their impacts on the quality of fermented foods. Microbial interactions primarily include mutualism, commensalism, amensalism, and competition. Based on these interaction patterns, the safety, nutritional composition, and flavor quality of food can be effectively improved. Achieving precise control of fermented foods’ qualities via microbial interaction remains a critical challenge. Emerging technologies such as high-throughput sequencing, cell sorting, and metabolomics enable the systematic analysis of core microbial interaction mechanisms in complex systems. Using synthetic microbial communities and genome-scale metabolic network models, complicated microbial communities can be effectively simplified. In addition, regulatory targets of food quality can be precisely identified. These strategies lay a solid foundation for the precise improvement of fermented food quality and functionality. Full article

Traumatic brain injury (TBI) is a major neurological condition affecting millions of individuals each year. Mild TBI (mTBI) manifests differently, with some individuals experiencing persistent, debilitating symptoms while others recover more rapidly. Despite its classification as “mild,” mTBI leads to both short- and long-term neurological effects, many of which occur due to functional changes in the brain. TBI-induced environmental changes within the brain play a critical role in shaping these functional outcomes. The importance of astrocytes in maintaining central nervous system (CNS) homeostasis has been increasingly recognized for their pivotal role in the brain’s response to TBI. Previous studies showed significant TBI-associated metabolic dysregulations. Therefore, we sought to analyze how astrocytes might adapt to persistent metabolic stressors in the post-injury microenvironment and identify injury-induced shifts occurring in vivo that may contribute to chronic metabolic dysfunction. We used an astrocyte-specific genome-scale metabolic model that allowed for the input of biologically relevant uptake rates corresponding to healthy astrocytes to analyze how the activity of metabolic pathways differed in hypoxic and acidic conditions. Additionally, these fluxes were integrated with mass spectrometry-based proteomics from male Sprague-Dawley rats subjected to mTBI to identify chronic adaptive neural responses post-injury. Comparison of modeled metabolic fluxes and experimental proteomic data demonstrated remarkable alignment, with both predicting significant changes in key metabolic processes including glycolysis, oxidative phosphorylation, the TCA cycle, and the Pentose Phosphate Pathway. These overlapping signatures may represent core survival strategies, offering insight into metabolic priorities and potentially serving as biomarkers of injury adaptation or recovery capacity. Full article

The increasing global demand for energy and the urgent need to mitigate climate change have driven the search for sustainable alternatives to fossil fuels, with biofuels emerging as a promising solution. However, the low yields and inefficiencies in biofuel production processes necessitate advanced strategies to enhance their commercial viability. Metabolic engineering has become a pivotal tool in optimizing microbial pathways to improve biofuel production, addressing these challenges through innovative genetic and synthetic biology approaches. This review highlights the role of metabolic engineering in enhancing biofuel production by focusing on microbial engineering for lignocellulosic biomass utilization, strategies to overcome inhibitor effects, and pathway optimization for biofuels like n-butanol and iso-butanol. It also explores the production of advanced biofuels from fatty acid and isoprenoid pathways, emphasizing the use of model organisms such as Escherichia coli and Saccharomyces cerevisiae. Key insights include the application of CRISPR/Cas9 and multiplex automated genome engineering for precise genetic modifications, as well as metabolic flux analysis to optimize pathway efficiency. Additionally, the review discusses synthetic biology methodologies to rewire metabolic networks and improve biofuel yields, providing a comprehensive overview of current advancements and their implications for industrial-scale production. Full article

Methanotrophs hold significant potential in global methane mitigation and resource recovery. However, the limited rate of cell proliferation remains a significant constraint for large-scale applications. Therefore, screening efficient methanotrophic strains that are suitable for industrial applications to mitigate methane and exploring potential methane resource utilization pathways are of great importance for sustainable development. Gradient dilution and the streak plate method were employed to isolate methanotrophic strains from a previously domesticated methane-oxidizing microbial consortium. We isolated a highly efficient strain, M6, which exhibited a 230% increase in growth rate compared to the laboratory model strain Methylocystis bryophila (M. bryophila). Taxonomic analysis revealed that strain M6 is classified as Methylocystis parvus. Genomic data indicated a diverse range of metabolic functions. In addition to utilizing methane, strain M6 can also utilize citrate to generate energy and intermediate products, addressing issues related to insufficient methane supply or low methane mass transfer efficiency. Metabolic adaptability ensures the stability of its application. The optimal cultivation conditions for strain M6 were determined, characterized by mild and easily implementable parameters. Based on the analysis of the genome and metabolic pathways, strain M6 exhibits potential for the synthesis of bioproducts, such as proteins, lipids, and polyhydroxyalkanoates (PHAs), with the fermentation process not requiring cost-intensive carbon sources, making it both economical and sustainable. Full article

Lactic acid bacteria exopolysaccharides (EPS) have a variety of excellent biological functions and are widely used in the food and pharmaceutical industries. The complex metabolic system of lactic acid bacteria and the mechanism of EPS biosynthesis have not been fully analyzed, which limits the wider application of EPS. EPS synthesis is regulated by cyclic diadenosine monophosphate (c-di-AMP), but the exact mechanism remains unclear. Dac and pde are c-di-AMP anabolic genes, gtfA, gtfB and gtfC are EPS synthesis gene clusters, among which gtfC was the key gene for EPS synthesis in Leuconostoc mesenteroides DRP105. In order to explore whether diadenylate cyclase (DAC) can catalyze the synthesis of c-di-AMP from ATP, the sequence of DAC was analyzed by bioinformatics based on the whole genome sequence. DAC was a CdaA type diadenylate cyclase containing the classical domain DisA_N and DGA and RHR motifs. The secondary structure was mainly composed of α-helices, and AlphaFold2 was used to model the 3D structure of the protein and evaluate the rationality of the DAC protein structure model. A total of 8 salt bridges, 21 hydrogen bonds and 221 non-bonded interactions were found between DAC and GtfC. Molecular docking simulations revealed ATP1 and ATP2 fully occupied the binding pocket of DAC and interacted directly with the binding site residues of DAC. The molecular dynamics simulations showed that the binding of DAC to ATP molecules was relatively stable. Gene and enzyme correlation analysis found that dac and gtfC gene expression were significantly positively correlated with DAC enzyme activity, c-di-AMP content and EPS production, and had no significant correlation with PDE enzyme activity responsible for c-di-AMP degradation. Bioinformatics analysis of the regulatory role of DAC in the synthesis of EPS by lactic acid bacteria was helpful to fully reveal the biosynthetic mechanism of EPS and provide theoretical basis for large-scale industrial production of EPS. Full article

Background/objectives: Amino acids (AAs) play a critical role in diseases such as cystic fibrosis where Pseudomonas aeruginosa PAO1 adapts its metabolism in response to host-derived nutrients. The adaptation influences virulence and complicates antibiotic treatment mainly for the antimicrobial resistance context. D- and L-AAs have been analyzed for their impact on quorum sensing (QS), a mechanism that regulates virulence factors. This research aimed to reconstruct the genome-scale metabolic model (GEM) of P. aeruginosa PAO1 to investigate the metabolic roles of D- and L-AAs in QS-related pathways. Methods: The updated GEM, iJD1249, was reconstructed by using protocols to integrate data from previous models and refined with well-standardized in silico media (LB, M9, and SCFM) to improve flux balance analysis accuracy. The model was used to explore the metabolic impact of D-Met, D-Ala, D-Glu, D-Ser, L-His, L-Glu, L-Arg, and L-Ornithine (L-Orn) at 5 and 50 mM in QS-related pathways, focusing on the effects on bacterial growth and carbon flux distributions. Results: Among the tested AAs, D-Met was the only one that did not enhance the growth rate of P. aeruginosa PAO1, while L-Arg and L-Orn increased fluxes in the L-methionine biosynthesis pathway, influencing the metH gene. These findings suggest a differential metabolic role for D-and L-AAs in QS-related pathways. Conclusions: Our results shed some light on the metabolic impact of AAs on QS-related pathways and their potential role in P. aeruginosa virulence. Future studies should assess D-Met as a potential adjuvant in antimicrobial strategies, optimizing the concentration in combination with antibiotics to maximize its therapeutic effectiveness. Full article

Background: As a complicated endocrine condition, polycystic ovarian syndrome affects around 20% of women who are of reproductive age. It is linked to an increased risk of endometrial cancer, cardiovascular diseases, mental illnesses, non-alcoholic fatty liver disease, metabolic syndrome, and Type 2 diabetes. Despite numerous genetic studies identifying several susceptibility loci, these only account for approximately 10% of the hereditary factors contributing to PCOS, leaving its etiology largely unknown. While genome-wide association studies (GWAS) have been conducted on various populations to identify SNPs linked to PCOS risk, no such study has been reported in Tabuk. Thus, this study aims to investigate the association of a glutathione S-transferase M1 (GSTM1) deletion, VEGF gene (I/D) insertion/deletion, and VEGF-2578 gene polymorphism with polycystic ovarian syndrome. Methodology: In this research study (case-control), we utilized the ARMS-PCR to determine and analyze the polymorphic variants of VEGF-2578 C/A (rs699947). We employed multiplex PCR for the GSTM1 deletion and MS-PCR (mutation specific PCR) for the vascular endothelial growth factor gene insertion/deletion. Results: The findings indicated statistically significant differences in various biochemical and endocrine serum biomarkers, including lipid profiles (cholesterol, HDL, and LDL), Type 2 diabetes markers (HOMA-IR (Homeostatic Model Assessment for Insulin Resistance), free insulin fasting glucose), and hormone levels (testosterone, LH, progesterone and FSH) in PCOS patients. Specifically, regarding the GSTT1 genotype, individuals with the GSTT1-null genotype had an odds ratio (OR) of 4.16 and a relative risk (RR) of 2.14 compared to those with the GSTT1 genotype, with statistically significant differences (p = 0.0001). However, for the GSTM1 genotype, there was a statistically significant difference (p = 0.0002) in the OR and RR for the GSTM1-null genotype, which were 2.66 and 1.64, respectively. Protective effects were observed for individuals with either GSTT1 (+) GSTM1 (−) or GSTT1 (−) GSTM1 (+) genotypes, as well as for those with both null genotypes, yielding an OR of 0.41 and p < 0.003. The VEGF rs699947 C>A gene variation showed a statistically significant association between PCOS patients and controls (p < 0.020), with the A allele frequency higher among PCOS patients (0.42 vs. 0.30). Similarly, the VEGF rs4646994 I>D gene variation exhibited a statistically significant difference (p < 0.0034), with the D allele being more frequent in PCOS patients (0.52 vs. 0.35). The VEGF-A allele was strongly linked to PCOS susceptibility in the allelic model, exhibiting an OR of 1.62, RR of 1.27, and p < 0.007, while in the allelic comparison, the OR was 1.71, the RR was 1.32, and p < 0.004. Conclusions: This study concluded that null genotypes at rs4025935 and rs71748309, an insertion deletion at rs4646994, and the A allele of rs699947 were significantly associated with PCOS predisposition in our population and these could serve as potential loci for PCOS predisposition. To the best of our knowledge, it is the first study to highlight the association between these genetic variations and the predisposition of PCOS in our populations. Large-scale case-control studies in the future are required to confirm these results. Full article

Background/Objectives: Determining appropriate cellular objectives is crucial for the system-scale modeling of biological networks for metabolic engineering, cellular reprogramming, and drug discovery applications. The mathematical representation of metabolic objectives can describe how cells manage limited resources to achieve biological goals within mechanistic and environmental constraints. While rapidly proliferating cells like tumors are often assumed to prioritize biomass production, mammalian cell types can exhibit objectives beyond growth, such as supporting tissue functions, developmental processes, and redox homeostasis. Methods: This review addresses the challenge of determining metabolic objectives and trade-offs from multiomics data. Results: Recent advances in single-cell omics, metabolic modeling, and machine/deep learning methods have enabled the inference of cellular objectives at both the transcriptomic and metabolic levels, bridging gene expression patterns with metabolic phenotypes. Conclusions: These in silico models provide insights into how cells adapt to changing environments, drug treatments, and genetic manipulations. We further explore the potential application of incorporating cellular objectives into personalized medicine, drug discovery, tissue engineering, and systems biology. Full article