Metabolic Model Research Articles

Streptococcus suis is an emerging zoonotic bacterial disease with increasing prevalence in the human population and is one of the most important bacterial infections in pig husbandry. There is still a lack of a thorough understanding of S. suis metabolism and the connection between metabolism and virulence. A genome-scale metabolic model iNX525, which included 525 genes, 708 metabolites, and 818 reactions, was manually constructed with a 74% overall MEMOTE score. The flux balance analysis results of the model exhibited good agreement with growth phenotypes under different nutrient conditions and genetic disturbances. The model predictions aligned with 71.6%, 76.3%, and 79.6% of the gene essentiality predictions from three mutant screens. The model was then used to analyze virulence factors and related synthetic pathways. One hundred and thirty-one virulence-linked genes were found by comparing to virulence factor databases, and among them, seventy-nine virulence-linked genes were in 167 metabolic reactions in model iNX525. One hundred and one of the metabolic genes were predicted to affect the formation of nine virulence-linked small molecules. Complex interrelationships between growth- and virulence-associated pathways were evaluated, and 26 genes were found to be essential for both cell growth and virulence factor production. Among these, eight enzymes and metabolites were identified as antibacterial drug targets, focusing on the biosynthesis of capsular polysaccharides and peptidoglycans. Overall, the metabolic model iNX525 provides a high-quality platform for systematic elucidation of the metabolism of S. suis.

Background: Drug-induced cardiotoxicity is a major cause of clinical trial failures and post-market drug withdrawals. Current screening methods rely primarily on cell assays and transcriptomic profiling, but metabolic perturbations may provide additional predictive signals for cardiotoxic liability in drugs not yet tested in humans. Hypothesis: We hypothesize that predicted metabolic flux changes derived from gene expression data would outperform transcriptomic features for predicting drug cardiotoxicity in a machine learning framework. Methods: We developed a novel computational pipeline integrating a modified iCardio genome-scale metabolic model with transcriptomic data from 5 genetically distinct hiPSC-derived cardiomyocyte cell lines treated with a variety of 31 antineoplastic and immunomodulating drugs (accessed from DToxS Center). Our mathematical framework converts gene expression changes to enzyme activity, then to relative metabolic reaction flux change (4122 reactions) using a novel constrained quadratic approach. Ensemble classifiers were trained to predict cardiotoxicity using FDA Adverse Event Reporting System Reporting Odds Ratio (ROR) as a reference, with drugs above median ROR classified as cardiotoxic. Predictive models were generated using 5-fold cross validation for hyperparameter optimization with 25% hold out for quality metric calculation (reported as mean ± SEM, P values calculated from t-test) over 100 independent iterations. Results: The metabolic flux-based classifiers demonstrated fair predictive performance of drug cardiotoxicity with an AUROC of 0.70±0.02. The flux approach produced equivalent accuracy (+0.02, P = 0.56), and significantly higher F1 (+0.10, P = 0.01), AUROC (+0.08, P = 0.01), and AUPRC (+0.07, P = 0.03) than the gene expression approach. Further analysis revealed that perturbations to fatty acid metabolism were most predictive of cardiotoxic liability, with 42 out of top 100 predictive reactions belonging to fatty acid related subsystems ( P < 1e-5 by binomial test). Conclusions: Metabolic flux prediction from transcriptomic data provides enhanced discrimination of drug cardiotoxicity compared to gene expression analysis alone. This approach enables more informative pre-clinical screening of drug candidates before human exposure, potentially reducing late-stage clinical failures and improving drug safety assessment protocols.

Background: Cardiovascular-Kidney-metabolic (CKM) syndrome is a public health problem in the US and results in premature CVD at a relatively preserved GFR. The molecular mediators of CKM are poorly understood, partly due to the lack of a reliable animal model. We set out to generate an animal model with renal and metabolic dysfunctions, using peripheral arterial disease (PAD) as a CKM manifestation. Methods: C57BL/6 male and female mice were randomized into four groups: a normal diet (ND, controls), a 0.2% adenine diet (AD, a CKD model), a high-fat diet (HFD, a metabolic model), and a combination of HFD+AD (a potential CKM model). The mice underwent a hind limb ischemia, followed by structural, endurance, and post-exercise hyperemia assays. Results: Compared to control mice, HFD+AD male mice had 23-50% higher weight and GFR than the AD group (P = 0.003). The kidneys of HFD+AD showed tubular atrophy, tubulointerstitial fibrosis, immune infiltration, glomerulomegaly, consistent with glomerular hyperperfusion, hypercholesterolemia, impaired glucose tolerance, and adipophilin in the liver, an early marker of hepatic steatosis, and myocardial fibrosis. The HFD+AD mice showed reduced hind limb perfusion ratios, microcapillary density, Type II muscle fibers, and increased muscle fibrosis, immune infiltration, and the lowest cross-sectional muscle area. Female CKM mice revealed distinct differences from male mice. Compared to AD and HFD alone, female CKM mice exposed to HFD+AD demonstrated additive phenotypes in endurance assays (distance travelled, exhaustion time and grip strength) without a similar effect in post-ischemia perfusion, suggesting skeletal muscle and microcapillary dysfunction. Conclusion: A combination of HFD+AD in mice displays CKD, metabolic disorders, and cardiovascular disease features at a higher GFR, consistent with human CKM. This model can be explored to probe the mechanisms, heterogeneity, and sex-specific differences in CKM.

The extracellular matrix is dynamic in composition and performs protective, trophic, supportive, and regulatory functions. Degradation of individual extracellular matrix components will lead to metabolic disorders of the entire muscle tissue. The progression of metabolic syndrome is accompanied by changes in the quantitative and qualitative composition of extracellular matrix. Activation of p38 mitogen-activated protein kinase also leads to increased production of extracellular matrix and changes its qualitative composition. Currently, the role of p38 mitogen-activated protein kinase activation during metabolic syndrome in the processes of skeletal muscle extracellular matrix degradation is not well understood. The aim of this work was to determine the effect of the administration of a specific inhibitor of p38 mitogen-activated protein kinase (SB203580) on the content of glycosaminoglycans, their individual fractions, the concentration of free L-hydroxyproline and sialic acids in the biceps femoris muscle of rats under conditions of metabolic syndrome modeling. The study was conducted on 24 sexually mature male Wistar rats weighing 200-260 g. The animals were randomly divided into 4 groups of 6 animals each. Group I – control. Group II – MetS group. MetS was reproduced by adding 20% fructose solution to the standard vivarium diet as the sole source of drinking water for 60 days. Group III – group of SB203580 administration at a dose of 2 mg/kg intraperitoneally once every 3 days for 60 days. Group IV – the group of combined effects of SB203580 administration and MetS reproduction. Animals were removed from the experiment under thiopental anesthesia by taking blood from the right ventricle of the heart. The object of the study was a 10% homogenate of rat biceps muscle, in which the total content of glycosaminoglycans, the content of individual glycosaminoglycans fractions, free L-hydroxyproline and sialic acids were studied. The combination of p38 MAPK blockade by administration of SB203580 and MetS modeling reduces the total glycosaminoglycans content in the biceps femoris muscle of rats by 22.0% compared to the MetS group. The content of the heparin-heparan fraction under these conditions increases by 24.4%, and the concentration of the chondroitin and keratan-dermatan fractions of glycosaminoglycans decreases by 28.8% and 38.6%, respectively, when compared with the indicators of the MetS group. The content of free L-hydroxyproline and sialic acids decreases by 19.2% and 15.8%, respectively, compared to the MetS group. Activation of p38 mitogen-activated protein kinase in the biceps femoris muscle of rats under conditions of metabolic syndrome prevents the degradation of proteoglycans and glycoproteins, reduces the intensity of collagen fiber breakdown, and promotes the restoration of the fractional composition of glycosaminoglycans. This work is a fragment of the initiative SRW No. 0124U000092 “High- and low-intensity phenotypes of systemic inflammatory response: molecular mechanisms and new medical technologies for their prevention and correction.”

Background: The FINEARTS-HF trial demonstrated that finerenone significantly reduced cardiovascular death and total heart failure events in patients with mildly reduced or preserved ejection fraction compared to placebo. However, the pharmacological mechanisms by which this highly selective, non-steroidal mineralocorticoid receptor antagonist (MRA) exerts cardioprotective effects in heart failure with preserved ejection fraction (HFpEF) remain insufficiently defined. Recent studies have indicated that mineralocorticoid receptor (MR) and glucocorticoid receptor (GR) not only interact but also mutually regulate their signaling across multiple organs, including the myocardium. Methods: A murine HFpEF model was established by inducing obesity, diabetes, and metabolic dysfunction-associated fatty liver disease (MAFLD) in conjunction with hypertension. Wild-type C57BL/6J mice were fed a high-fat diet and administered NG-nitro-L-arginine methyl ester (L-NAME) for 10 weeks. During the pathological hypertrophy phase, mice were treated with either finerenone or placebo for two weeks via oral gavage. Cardiac function and arterial pressure were evaluated using transthoracic echocardiography and continuous invasive blood pressure monitoring. Myocardial tissue underwent histological evaluation, immunoblotting, and transcriptomic analysis from cardiomyocyte-enriched nuclear fractions to elucidate finerenone-specific cardiac effects. Results: Combined metabolic and hypertensive stress induced obesity, glucose intolerance, elevated blood pressure, and MAFLD, alongside increased MR nuclear localization in cardiomyocytes. Despite no significant group difference in 24-hour arterial pressure, finerenone-treated mice exhibited significantly reduced cardiomyocyte cross-sectional area (placebo: 235.9 ± 41.7 μm^2; finerenone: 213.7 ± 60.3 μm^2; p = 0.0189) and preserved capillary density. RNA sequencing revealed that finerenone suppressed expression of hypertrophic gene signatures while maintaining GR target gene expression associated with cardiac resilience. Protein analysis confirmed that GR nuclear translocation was preserved in the finerenone group. Conclusion: Finerenone mitigates cardiomyocyte hypertrophy in a metabolic HFpEF model, independent of blood pressure reduction. This effect may be attributable to rebalancing MR and GR signaling in the myocardium, offering novel insight into finerenone’s mode of action beyond mineralocorticoid blockade.

Background: The Warburg effect, historically regarded as a hallmark of cancer metabolism, is often interpreted as a universal metabolic feature of tumor cells. However, accumulating experimental evidence challenges this paradigm, revealing a more nuanced and context-dependent metabolic landscape. Methods: In this study, we present a hybrid multiscale model of tumor metabolism that integrates cellular and environmental dynamics to explore the emergence of metabolic phenotypes under varying conditions of stress. Our model combines a reduced yet mechanistically informed description of intracellular metabolism with an agent-based framework that captures spatial and temporal heterogeneity across tumor tissue. Each cell is represented as an autonomous agent whose behavior is shaped by local concentrations of key diffusive species—oxygen, glucose, lactate, and protons—and governed by internal metabolic states, gene expression levels, and environmental feedback. Building on our previous work, we extend existing metabolic models to include the reversible transport of lactate and the regulatory role of acidity in glycolytic flux. Results: Simulations under different environmental perturbations—such as oxygen oscillations, acidic shocks, and glucose deprivation—demonstrate that the Warburg effect is neither universal nor static. Instead, metabolic phenotypes emerge dynamically from the interplay between a cell’s history and its local microenvironment, without requiring genetic alterations. Conclusions: Our findings suggest that tumor metabolic behavior is better understood as a continuum of adaptive states shaped by thermodynamic and enzymatic constraints. This systems-level perspective offers new insights into metabolic plasticity and may inform therapeutic strategies targeting the tumor microenvironment rather than intrinsic cellular properties alone.

Treatment options for advanced liver disease and hepatocellular carcinoma (HCC) are limited, and strategies to prevent HCC development are lacking. Aiming to discover therapeutic targets, we combined genome-wide transcriptomic analysis of liver tissues from patients with advanced liver disease and HCC and a cell-based system predicting liver disease progression and HCC risk. Computational analysis predicted peroxiredoxin 2 (PRDX2) as a candidate gene mediating hepatocarcinogenesis and HCC risk. Analysis of tissues from patients with HCC confirmed a perturbed expression of PRDX2 in cancer. In vivo perturbation studies in mouse models for hepatocarcinogenesis driven by metabolic dysfunction-associated steatohepatitis showed that specific Prdx2 KO in hepatocytes improved metabolic liver functions, restored AMPK activity, and prevented HCC development by suppressing oncogenic signaling. Perturbation studies in HCC cell lines, a cell line-derived xenograft mouse model, and patient-derived HCC spheroids revealed that PRDX2 also mediates cancer initiation, cancer cell proliferation, and survival through its antioxidant activity. Targeting PRDX2 may therefore be a strategy to prevent HCC development in metabolic liver disease.

Parkinson’s disease (PD) is a neurodegenerative disorder characterized by motor symptoms like tremor, rigidity, and bradykinesia. The WHO estimates that 10 million people currently have PD, with its prevalence expected to double to 20 million by 2050. Key risk factors include age, male sex, environmental contaminants, and family history. Emerging evidence links gut microbiota dysbiosis to PD, suggesting it contributes to neuroinflammation and disease progression, though the role of dietary interventions remains unclear. This study used computational simulations with genome-scale metabolic models (GEMs) to analyze how diet impacts the gut microbiota in PD patients. Fecal microbiota from PD patients and healthy controls were compared across three diets: high-fiber, Mediterranean, and vegan. Simulations revealed increased pro-inflammatory bacteria (e.g., Escherichia coli O157) in PD patients, likely due to reduced bacterial competition, alongside the decreased production of beneficial metabolites like butyrate, phenylalanine, and cysteine. The Mediterranean diet showed higher short-chain fatty acid production, potentially benefiting PD patients. These findings underscore the importance of dietary interventions in modulating the gut microbiome and suggest that targeted diets may complement PD therapies, improving patient outcomes.

Guide the design of lactic acid bacteria synthesis community through computational metabolic interaction experimental pipeline.

L-5-[11C]-glutamine PET of breast cancer: Preclinical studies in mouse models.

Chronic intracellular bacterial infections persist within host cells by evading immune clearance, imposing prolonged metabolic stress on the host. In response, the immune system undergoes metabolic reprogramming to sustain prolonged defense. A key feature of this reprogramming is the shift from oxidative phosphorylation (OXPHOS) to aerobic glycolysis, which enhances pro-inflammatory and antimicrobial responses. Concurrently, fatty acid and amino acid catabolism provide additional metabolic support. Beyond shaping immune function, these metabolic shifts also influence the trajectory of infection by altering the host-pathogen metabolic interplay. In this review, we focus primarily on Mycobacterium tuberculosis (Mtb) infection and integrate quantitative flux analyses of carbon and nitrogen distribution, emphasizing how these metabolic changes connect to epigenetic regulation. We also explore metabolic reprogramming in five representative immune cell types-comprising both innate and adaptive immune cells-to elucidate how their distinct metabolic profiles influence host defense mechanisms and disease progression. Building on these foundations, we propose an innovative metabolic competition model between host and pathogen, offering new insights into the intricate interplay of metabolic networks in chronic intracellular infections.

Microbiota-derived butyrate promotes a FOXO1-induced stemness program and preserves CD8+ T cell immunity against melanoma.

Reprogramming Corynebacterium glutamicum metabolism to efficiently synthesis protocatechuic acid from flask to pilot scale.

Biomass accumulation in chondrocyte metabolic modelling: Incorporating extracellular matrix proxies to predict tissue engineering outcomes.

Digestive and absorptive properties of human milk fat substitute evaluated by in vitro and in vivo models.

Pan-reactome analysis of Streptomyces strains reveals association and disconnection between primary and secondary metabolism.

Multimodal large language models and mechanistic modeling for glucose forecasting in type 1 diabetes patients.

Microbial alcohol production from waste gases is a game changer for sustainable carbon cycling and remediation. While the biotechnological process using Clostridium autoethanogenum to transform syngas (H2, CO2 and CO) is blooming, scientific debates remain on the ethanol biosynthesis pathway. Here, we experimentally validated that ethanol production is initiated through a tungsten-dependent aldehyde:ferredoxin oxidoreductase (AFOR), which reduces acetate to acetaldehyde. The reaction, thermodynamically unfavorable under standard conditions, has been considered by many as unsuitable in vivo but is rather approved by metabolic modeling. To answer this riddle, we demonstrated that the thermodynamic coupling of CO oxidation and ethanol synthesis allows acetate reduction. The experiments, performed with native CO dehydrogenase and AFOR, highlighted the key role of ferredoxin in stimulating the activity of both metalloenzymes and electron shuttling. The crystal structure of holo AFOR, refined to 1.59-Å resolution, and its biochemical characterization provide new insights into the cofactor chemistry and the specificities of this enzyme, fundamental to sustainable biofuel production.

Demonstration of in vitro-in vivo relationship (IVIVR) and virtual bioequivalence (VBE) between progesterone intravaginal rings using physiologically based pharmacokinetic (PBPK) modelling.

Metabolic network modeling, especially Flux Balance Analysis (FBA), plays a critical role in systems biology by providing insights into cellular behaviors. Although FBA is the main tool for predicting flux distributions, it can face challenges capturing flux variations under different conditions. Selecting an appropriate objective function is therefore important for accurately representing system performance. To address this, we introduce a novel framework (e.g., TIObjFind) that imposes Metabolic Pathway Analysis (MPA) with Flux Balance Analysis (FBA) to analyze adaptive shifts in cellular responses throughout different stages of a biological system. This framework determines Coefficients of Importance (CoIs) that quantify each reaction's contribution to an objective function, aligning optimization results with experimental flux data. By examining Coefficients of Importance, TIObjFind enhances the interpretability of complex metabolic networks and provides insights into adaptive cellular responses.