Metabolic Model Research Articles

With the regulation of etomidate (ETO), criminals continue to use chemical modifications to derive various ETO analogues as substitutes to enter the illegal market in order to evade punishment, and the phenomenon of substitution abuse is becoming increasingly serious. After these substitutes are ingested by the human body, the prototype drug is almost undetectable in the biological samples. It is necessary to search for "characteristic" metabolite traceability prototype drugs to effectively prevent misjudgment of cases. The fragmentation patterns and metabolites of seven ETO analogs were analyzed by ultra high-performance liquid chromatography coupled with high-resolution mass spectrometry (HPLC-HRMS) and liquid chromatography tandem triple quadrupole mass spectrometry (HPLC-MS/MS) using an in vitro metabolic model of human liver microsomes. These seven compounds undergo I-phase metabolites, including imidazole ring disconnection from alkyl groups, benzene ring and alkyl group interactions, dealkylation, and so on. The parent compounds propoxate and sec-butomidate were not detected in the actual 83 hair and 4 urine samples tested; ETO and isopropoxate were the two main drugs detected in hair samples; and CF3-etomidate, a novel alternative, was detected in hair at a higher rate than metomidate. In addition, 47 hair samples showed a single parent substance, and 35 samples contained two or more drugs, indicating the possibility of mixed use of ETO and its analogues. The biomarkers of metomidate in both urine and hair are hydroxylated metabolites on the benzene ring; the biomarkers of isopropoxate undergo a hydroxylation reaction on the benzene ring (hair) and cleavage of the C-N bond between the imidazole ring and alkyl group (urine); the metabolic product of the hydroxylation reaction on alkyl groups is a biomarker of butomidate. In this study, the metabolite differences of seven ETO analogs were compared for the first time in order to infer the parent compounds based on biomarkers. Meanwhile, based on the existing knowledge of the metabolism of the seven known substances in this experiment, it is possible to make a preliminary prediction of emerging ETO analogs in order to improve the understanding of early warning systems and the timely updating of monitoring techniques.

Avian pathogenic Escherichia coli (APEC) are a genetically diverse pathotype primarily associated with extra-intestinal infections in birds. APEC lineages are predicted to have unique metabolic capabilities contributing to virulence and survival in the host environment. Here, we present a genome-scale metabolic model for the APEC pathotype based on 114 APEC genome sequences and lineage-specific models for the phylogroups B2, C and G based on a representative isolate for each phylogroup. A total of 1,848 metabolic reactions were predicted in the 114 APEC isolates before gap filling and manual correction. Of these, 89% represented core reactions, whilst the 11% accessory reactions were mostly associated with carbon and nitrogen metabolism. Predictions of auxotrophy were confirmed by inactivation of the conditionally essential lysA and the non-essential potE genes. The APEC metabolic model outperformed the E. coli K-12 iJO1366 model in the Biolog Phenotypic Array platform. Sub-models specific to phylogroups B2, C and G predicted differences in the metabolism of 3-hydroxyphenylacetate (3-HPAA), a phenolic acid derived from the flavonoid quercetin, which is commonly added to poultry feed. Two 3-HPAA-associated reactions/genes distinguished APEC phylogroup C from APEC phylogroups B2 and G, and 3-HPAA supported the growth of APEC phylogroup C in minimal media, but not phylogroups B2 and G. In conclusion, we have constructed genome-scale metabolic models for the three major APEC phylogroups B2, C and G and have identified a metabolic pathway distinguishing phylogroup C APEC. This demonstrates the importance of lineage- and pathotype-specific metabolic models when investigating genetically diverse microbial pathogens.

Understanding the complex interactions between microbes and their environment requires robust model systems such as synthetic communities (SynComs). We developed a functionally directed approach to generate SynComs by selecting strains that encode key functions identified in metagenomes. This approach enables the rapid construction of SynComs tailored to any ecosystem. To optimize community design, we implemented genome-scale metabolic models, providing in silico evidence for cooperative strain coexistence prior to experimental validation. Using this strategy, we designed multiple host-specific SynComs, including those for the rumen, mouse, and human microbiomes. By weighting functions differentially enriched in diseased versus healthy individuals, we constructed SynComs that capture complex host–microbe interactions. We designed an inflammatory bowel disease SynCom of 10 members that successfully induced colitis in gnotobiotic IL10−/− mice, demonstrating the potential of this method to model disease-associated microbiomes. Our study establishes a framework for designing functionally representative SynComs of any microbial ecosystem, facilitating mechanistic study.

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Data-driven models of the glucose-insulin metabolism have recently emerged as an effective framework for realistic virtual patient modeling in diabetes. The growing demand for personalized therapies requires precise and individualized models that align naturally with machine learning models trained on patient-specific data. Using deep generative models such as generative adversarial networks opens new possibilities for incorporating previously unmodeled physiological phenomena into simulations. In this study, we developed a new extended version of our conditional Wasserstein generative adversarial network model by incorporating aerobic exercise intensity data from the T1DEXI dataset, along with insulin administration and carbohydrate consumption data. We use an aerobic physical activity model to describe the effects of immediate and prolonged exercise on glycemia from recorded discrete intensity levels. This enables the network to retain contextual information about recent aerobic physical activity. A total of 1479 days of data from 56 patients, including 308 exercise sessions, were used to train and validate our model. We evaluated the model to ensure that it replicates real-world data from the T1DEXI study in terms of mean blood glucose, time below range, time in range, time above range, and time in tight range, both in aggregate and when separated by active and sedentary days. In addition, the model reproduces aerobic exercise-induced glucose drops. This new model provides a more reliable, extended framework for in silico trials that incorporate physical activity scenarios, which has the potential to be used in the design and validation of automated insulin delivery.

The gut microbiome is increasingly linked to systemic health and central nervous system disorders, including brain tumors. This study investigated gut microbiome composition and metabolic profiles in patients with sellar-parasellar tumors (SPTs), other brain tumor types (OBTs) and healthy controls (HCs) to identify microbial and metabolic biomarkers for brain tumor phenotypes. A cross-sectional study involving 56 participants (17 SPTs, 11 OBTs, 28 HCs) was conducted. Gut microbiota composition was analyzed with 16S rRNA sequencing, and metabolic activity was inferred via metagenome-scale metabolic models. Multivariable regression and machine learning were used to evaluate microbial and metabolic differences across groups. Taxonomic and metabolic analyses revealed distinct profiles across these groups. The result showed that HCs exhibited higher levels of Lachnospira and Comamonadaceae, while tumor patients had an over-representation of Bacilli. OBT patients showed elevated metabolic exchange scores (MES) for amino acids (D-alanine, L-glutamic acid), carbohydrates (mucin-type O-glycans, alpha-lactose), and lipids (stearic acid, choline), most likely reflecting tumor-associated metabolic demands. Conversely, SPT patients had profiles closer to HCs, with lower MES and reduced systemic disruption. Key taxa such as Akkermansia, Faecalibacterium, and Lachnospira demonstrated tumor-specific adaptive metabolic outputs, emphasizing functional microbial contributions over purely taxonomic roles. These findings highlight the role of gut microbiota in brain tumor progression through altered metabolic pathways, suggesting potential biomarkers and therapeutic targets for neuro- oncology. The study integrates genome-scale metabolic modeling with 16S profiling to show that functional metabolic divergence can exist even when taxonomic differences are subtle, revealing overlooked biomarkers of the gut-brain axis in neuro-oncology.

The NOD‑like receptor family pyrin domain containing 3 (NLRP3) inflammasome is a key molecular complex that amplifies inflammatory cascades by maturing interleukin‑1 beta (IL-1β) and interleukin‑18 (IL-18) and inducing pyroptosis. It serves as a major driver and co-driver of numerous diseases associated with chronic inflammation. Dysregulated NLRP3 activation contributes to the progression of disorders such as rheumatoid arthritis, inflammatory bowel disease, neurodegenerative diseases and atherosclerosis. Curcumin, a natural polyphenol derived from Curcuma longa, offers a promising approach to inhibit NLRP3-induced inflammation owing to its multi-targeted actions and excellent safety profile. Preclinical models have demonstrated that curcumin inhibits nuclear factor kappa‑light‑chain‑enhancer of activated B cells (NF-κB) signaling, reduces mitochondrial reactive oxygen species (ROS) generation, and suppresses caspase-1 activation and apoptosis-associated speck-like protein containing a caspase recruitment domain (ASC) assembly, thereby inhibiting inflammasome activation. Curcumin has successfully prevented IL-1β-induced biological effects, tissue damage, and clinical manifestations in models of arthritis, colitis, and Alzheimer's disease (AD). In addition, advanced nanoformulations and structural analogs have enhanced their bioavailability and therapeutic reach. Here, we present a mechanistically focused, curcumin-oriented review synthesizing current knowledge on the NLRP3 inflammasome and its role in chronic inflammatory diseases. We also critically evaluated nanoformulations, curcumin analogs, and combination therapies and integrated evidence from rheumatologic, gastrointestinal, neurodegenerative, metabolic, and cardiovascular models. Furthermore, we explored the molecular mechanisms underlying the therapeutic effects of curcumin and highlighted the challenges of its clinical translation, offering insights for designing precision anti-inflammasome strategies to advance inflammation therapeutics.

Urinary tract infections (UTIs) are among the most common bacterial infections and are increasingly complicated by multidrug resistance (MDR). While Escherichia coli is frequently implicated, the contribution of broader microbial communities remains less understood. Here, we integrate metatranscriptomic sequencing with genome-scale metabolic modeling to characterize active metabolic functions of patient-specific urinary microbiomes during acute UTI. We analyzed urine samples from 19 female patients with confirmed uropathogenic E. coli (UPEC) infections, reconstructing personalized community models constrained by gene expression and simulated in a virtual urine environment. This systems biology approach revealed marked inter-patient variability in microbial composition, transcriptional activity, and metabolic behavior. We identified distinct virulence strategies, metabolic cross-feeding, and a modulatory role for Lactobacillus species. Comparisons between transcript-constrained and unconstrained models showed that integrating gene expression narrows flux variability and enhances biological relevance. These findings highlight the metabolic heterogeneity of UTI-associated microbiota and point to microbiome-informed diagnostic and therapeutic strategies for managing MDR infections.

Microbial fermentation of syngas (CO, H2, CO2) using acetogens is a promising route for the revalorisation of one-carbon feedstocks. However, product diversification from syngas using pure cultures of these microorganisms remains a challenge. Here, we present a synthetic microbial community comprising the acetogen Acetobacterium wieringae JM, the propionigenic bacterium Anaerotignum neopropionicum and the chain elongator Clostridium kluyveri, which collectively produce odd- and even-chain carboxylic acids and higher alcohols from CO/CO2. In batch bioreactors, metabolite cross-feeding within the community enabled the production of valerate (0.61 g L-1) and pentanol (0.33 g L-1), which are rare products in CO-fermenting systems. Chemostat experiments showed a metabolic shift induced in the acetogen by the ethanol-consuming species. Furthermore, construction of the genome-scale metabolic model (GEM) of A. wieringae JM and a community model of the triculture allowed us to predict the performance of the culture in continuous (steady-state) process. Simulations using flux balance analysis predicted a feasible triculture with A. wieringae JM dominating the community, and provided insights into the effect of H2 supplementation on the product spectrum. The results of our study underscore the potential of synthetic microbial communities for syngas fermentation, with genome-scale metabolic modeling serving as a powerful tool to identify metabolic shifts and guide experimental design.

Genome-scale metabolic models (GEMs) are widely used in systems biology to investigate metabolism and predict perturbation responses. Automatic GEM reconstruction tools generate GEMs with different properties and predictive capacities for the same organism. Since different models can excel at different tasks, combining them can increase metabolic network certainty and enhance model performance. Here, we introduce GEMsembler, a Python package designed to compare cross-tool GEMs, track the origin of model features, and build consensus models containing any subset of the input models. GEMsembler provides comprehensive analysis functionality, including identification and visualization of biosynthesis pathways, growth assessment, and an agreement-based curation workflow. GEMsembler-curated consensus models built from four Lactiplantibacillus plantarum and Escherichia coli automatically reconstructed models outperform the gold-standard models in auxotrophy and gene essentiality predictions. Optimizing gene-protein-reaction (GPR) combinations from consensus models improves gene essentiality predictions, even in the manually curated gold-standard models. GEMsembler explains model performance by highlighting relevant metabolic pathways and GPR alternatives, informing experiments to resolve model uncertainty. Thus, GEMsembler facilitates building more accurate and biologically informed metabolic models for systems biology applications.IMPORTANCEGenome-scale metabolic models (GEMs) capture our knowledge of cellular metabolism as encoded in the genome, enabling us to describe and predict how cells function under different conditions. While several automated tools can generate these models directly from genome data, the resulting models often contain gaps and uncertainties, highlighting areas where our metabolic knowledge is incomplete. Here, we introduce a new tool called GEMsembler, which integrates GEMs constructed by different methods, evaluate model uncertainty, and build consensus models, harnessing the unique features of each approach. These consensus models more accurately reflect experimentally observed metabolic traits, such as nutrient requirements and condition-specific gene essentiality. GEMsembler facilitates comprehensive analysis of model structure and function, helping to pinpoint knowledge gaps and prioritize experiments to address them. By synthesizing information from diverse sources, GEMsembler accelerates the development of more reliable and biologically meaningful models, advancing research in metabolic engineering, pathogen biology, and microbial community studies.

Dinitrogen (N2) fixation provides bioavailable nitrogen to the biosphere. However, in some habitats (e.g., sediments), the metabolic pathways of organisms carrying out N2 fixation are unclear. We present metabolic models representing various chemotrophic N2 fixers, which simulate potential pathways of electron transport and energy flow, resulting in predictions of whole-cell stoichiometries. By balancing mass, electrons, and energy for metabolic half-reactions, we quantify the electron usage for nine N2 fixers. Our results demonstrate that all modeled organisms fix sufficient N2 for growth. Aerobic organisms allocate more electrons to N2 fixation and growth, yielding more biomass and fixing more N2, while methanogens using acetate and organisms using sulfate allocate fewer electrons. This work can be applied to investigate the depth distribution of N2 fixers based on nutrient availability, complementing field measurements of biogeochemical processes and microbial communities.IMPORTANCEN2 fixation is an important process in the global N cycle. Researchers have developed models for heterotrophic and photoautotrophic N2 fixers, but there is a lack of modeling studies on chemoautotrophic N2 fixers. Here, we built nine biochemical models for different chemoautotrophic N2 fixers by combining different types of half-chemical reactions. We include three sulfide oxidizers using different electron acceptors (O2, NO3-, and Fe3+), contributing to the sulfur, nitrogen, and iron cycles in the sediment. We have two methanogens using different substrates (H2 and acetate) and four methanotrophs using different electron acceptors (O2, NO3-, Fe3+, and SO42-). By modeling these methane producers and users in the sediment and their N2-fixing metabolic pathways, our work can provide insight for future carbon cycle studies. This study outlines various metabolic pathways that can facilitate N2 fixation, with implications for where in the environment they might occur.

SummaryComparative molecular and physiological analyses of organisms from one taxonomic group grown under similar conditions offer a strategy to identify gene targets for trait improvement. While this strategy can also be performed in silico using genome‐scale metabolic models for the compared organisms, we continue to lack solutions for the de novo generation of such models, particularly for eukaryotes.To facilitate model‐driven identification of gene targets for growth improvement in green algae, here we present a semiautomated platform for de novo generation of genome‐scale algal metabolic models. We deployed this platform to reconstruct an enzyme‐constrained, genome‐scale metabolic model of Chlorella ohadii, the fastest growing green alga reported to date, and validated the growth predictions in experiments under three growth conditions. We also proposed a computational strategy to identify targets for growth improvement based on flux analyses.Extensive flux‐based comparative analyses using all existing models of green algae resulted in the identification of potential targets for growth improvement not only in standard but also in extreme light conditions, where C. ohadii still exhibits exceptional growth.Our findings indicate that the developed platform provides the basis for the generation of pan‐genome‐scale metabolic models of algae.

SC05-UT is an anaerobic, heterogenous microbial enrichment culture that reduces chloroform to dichloromethane through reductive dechlorination, which it further mineralizes to carbon dioxide. This dichloromethane mineralization yields electron equivalents that are used to reduce chloroform without the addition of exogenous electron donor. By studying this self-feeding chloroform-amended culture and a dichloromethane-amended enrichment subculture (named DCME), we previously found the genomic potential to perform both biodegradation steps in two distinct Dehalobacter strains: Dehalobacter restrictus SAD and Candidatus Dehalobacter alkaniphilus DAD. Though present in each enrichment culture, strain SAD is more abundant in the chloroform-fed subculture SC05-UT, while strain DAD is more prominent in the dichloromethane-fed subculture DCME. To understand if genomic differences between strains impact their metabolic mechanisms, the genome of each strain was curated to reconstruct genome-scale metabolic models of each strain, which were then constrained based on thermodynamic and experimental conditions. We demonstrate that metabolic differences between the two strains may allow Dehalobacter strain DAD to outcompete strain SAD in the absence of chloroform, while strain SAD exhibits an advantage in the presence of chloroform. Additionally, we predict electron cycling methods to reconcile cellular redox imbalances during tandem chloroform and dichloromethane dechlorination. This work highlights the importance of hydrogen and amino acid exchange in these microbial communities and contributes to the growing body of work surrounding organohalide syntrophy.IMPORTANCEChloroform and dichloromethane contaminate groundwater around the world but can be remediated by microbes capable of metabolizing these toxic compounds. Here, we study two distinct strains of Dehalobacter and show that while both strains can degrade both chloroform and dichloromethane, differences in their genetic makeup allow each strain to thrive under different environmental conditions. This has implications for understanding the fate of halogenated methanes in the environment and the application of Dehalobacter for bioremediation of chlorinated compounds.

Intercellular cross-talk is essential for the adaptation capabilities of populations of cells. While direct diffusion-driven cell-to-cell exchanges are difficult to map, current nanotechnology enables one to probe single-cell exchanges with the medium. We introduce a mathematical method to reconstruct the dynamic unfolding of intercellular exchange networks from these data, applying it to an experimental coculture system. The exchange network, initially dense, progressively fragments into small disconnected clusters. To explain these dynamics, we develop a maximum-entropy multicellular metabolic model with diffusion-driven exchanges. The model predicts a transition from a dense network to a sparse one as nutrient consumption shifts. We characterize this crossover both numerically, revealing a power-law decay in the cluster-size distribution, and analytically, by connecting to percolation theory. Comparison with data suggests that populations evolve toward the sparse phase by remaining near the crossover. These findings offer insights into the collective organization driving the adaptive dynamics of cell populations.

Untangling metabolic interactions of a nongrowing, hydrogen producing synthetic coculture through a multispecies metabolic flux analysis.

The abuse of fentanyl-type new psychoactive substances (F-NPS), which exhibit the four defining characteristics of new psychoactive substances (third-generation drugs), poses a severe threat to social stability and public health. The derivatization strategy investigated in this study, involving six substituted acetyl F-NPS across two substitution patterns, represents the primary approach for generating a new F-NPS. Using an in vitro human liver microsome metabolic model coupled with liquid chromatography-ion trap tandem time-of-flight mass spectrometry, we identified characteristic metabolism profiles of F-NPS corresponding to derivatization modifications while elucidating the structural effects on metabolism. This study revealed that, first, metabolism via amide hydrolysis was affected by concurrent hydrolysis at adjacent positions, rather than being solely determined by carbonyl carbon electrophilicity. Second, metabolism via N-oxidation and N-dealkylation shared a common initial intermediate, with the latter being triggered by α-hydroxylation of the phenethyl group. Third, metabolism via N-oxidation exhibited reduced susceptibility to structural changes owing to the contradictory bond orientations of the substituents on the piperidine ring between the parent drug and its N-oxide metabolite. Fourth, stable geminal diol metabolites were identified in the substituted acetyl F-NPS metabolites via mass spectrometric fragmentation. This research deepens the understanding of structure-metabolism relationships among F-NPS, providing critical foundational data for developing predictive metabolisms for emerging F-NPS and offering scientific support for drug abuse surveillance and prevention strategies.

Genome-scale metabolic models (GEMs) have been widely utilized to understand cellular metabolism. The application of GEMs has been advanced by computational methods that enable the prediction and analysis of intracellular metabolic states. However, the accuracy and biological relevance of these predictions often suffer from the many degrees of freedom and scarcity of available data to constrain the models adequately. Here, we introduce Neural-net EXtracellular Trained Flux Balance Analysis, (NEXT-FBA), a novel computational methodology that addresses these limitations by utilizing exometabolomic data to derive biologically relevant constraints for intracellular fluxes in GEMs. We achieve this by training artificial neural networks (ANNs) with exometabolomic data from Chinese hamster ovary (CHO) cells and correlating it with 13C-labeled intracellular fluxomic data. By capturing the underlying relationships between exometabolomics and cell metabolism, NEXT-FBA predicts upper and lower bounds for intracellular reaction fluxes to constrain GEMs. We demonstrate the efficacy of NEXT-FBA across several validation experiments, where it outperforms existing methods in predicting intracellular flux distributions that align closely with experimental observations. Furthermore, a case study demonstrates how NEXT-FBA can guide bioprocess optimization by identifying key metabolic shifts and refining flux predictions to yield actionable process and metabolic engineering targets. Overall, NEXT-FBA aims to improve the accuracy and biological relevance of intracellular flux predictions in metabolic modelling, with minimal input data requirements for pre-trained models.

Resolving versus non-resolving sphingolipid dynamics during macrophage activation: a time-resolved metabolic analysis.

Evaluating rat and canine microbiota models for predicting human colonic prodrug metabolism.

Background and AimsMetabolic dysfunction-associated steatotic liver disease (MASLD) represents an escalating healthcare burden across the Middle East and North Africa (MENA) region; however, system-level preparedness remains largely undefined. This study aimed to assess existing models of care, clinical infrastructure, policy frameworks, and provider perspectives across 17 MENA countries.MethodsA cross-sectional, mixed-methods survey was distributed to clinicians from MASLD-related specialties across the region. A total of 130 experts (87.2% response rate) from academic, public, and private sectors in 17 countries participated. The questionnaire addressed national policies, diagnostic and therapeutic practices, referral pathways, multidisciplinary team (MDT) integration, and patient/public engagement. Quantitative responses were analyzed descriptively, while qualitative inputs underwent thematic analysis.ResultsOnly 35.4% of respondents confirmed the presence of national clinical guidelines for MASLD, and 73.1% reported the absence of a national strategy. Structured referral pathways were reported by 39.2% of participants, and only 31.5% believed the current model adequately addresses MASLD. While 60% supported MDT approaches, implementation remained inconsistent. Limited access to transient elastography was reported by 26.2% of providers. Public education efforts were minimal: 22.3% reported no available tools, and 87.7% indicated the absence of patient-reported outcomes data. Nearly half (47.7%) cited poor patient adherence, attributed to low awareness, financial barriers, and lack of follow-up.ConclusionsSignificant policy, structural, and educational gaps persist in MASLD care across the MENA region. To address this rising burden, countries must adopt integrated national strategies, expand access to non-invasive diagnostic tests, institutionalize MDT care, and invest in both public and provider education as essential pillars of system-wide preparedness.