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At least 19 recordsLinked to original sources

PubMed2026

Investigating overflow metabolism in heterotrophic cultures of the green alga Chromochloris zofingiensis.

Chromochloris zofingiensis is of interest for its ability to perform a reversible trophic switch in the presence of glucose that is characterized by a shutdown of photosynthesis and an accumulation of energy storage metabolites. Previous work has shown that this trophic switch is accompanied by overflow metabolism and the production of lactate in aerobic conditions. This trophic switch is not observed in nutrient replete media. We utilized isotopically assisted metabolic flux analysis to characterize intracellular flux distributions that are associated with different metabolic phenotypes observed in this organism in different media formulations in light and dark conditions. The results of this analysis showed that low iron cultures have no flux through carbon fixation reactions, and that the carbon flux entering the TCA cycle in these cultures is approximately 40 % lower than that in iron replete cultures grown heterotrophically. This analysis was complemented with transcriptomics data collected for C. zofingiensis grown in iron limited conditions to provide further evidence towards the negative impact of iron limitation on both photosynthetic and respiratory activity. Overflow metabolism allows this alga to compensate for the lower energy production that results from iron limitation. This work highlights how nutrient availability can lead to changes in the metabolism of C. zofingiensis.

Iron↗

PubMed2026

Exogenous lactate ameliorates Aβ-induced energy deficit and neurotoxicity with increased mitochondrial TCA cycle carbon flux in SH-SY5Y cells.

A growing body of evidence has demonstrated the existence of metabolic dysfunction in neurodegenerative diseases, including Alzheimer's disease (AD), suggesting that deprivation of energy substrates impairs cellular dynamics. As glucose utilization declines in patients with AD, the need for alternative energy sources becomes crucial to sustain neuronal activities and prevent cell death induced by neurotoxic proteins, such as amyloid beta (Aβ) aggregates. In this context, lactate has been investigated as a potential alternative brain energy substrate in several studies, yet its impact on neuronal cells under Aβ-induced toxicity remains unclear. We confirmed significant suppression of energy production-related biological pathways by analyzing brain transcriptomic data of patients with AD. In subsequent in vitro studies, exogenous lactate treatment ameliorated neuron-like cell death caused by Aβ aggregates. Using a 13C stable isotope tracer, we verified cellular lactate uptake and its incorporation into tricarboxylic acid (TCA) cycle in neurons under the neurotoxic condition. 13C metabolic flux analysis further supported these findings by revealing that lactate treatment restored Aβ-suppressed mitochondrial TCA cycle fluxes. These metabolic improvements were accompanied by increased expression of mitochondrial proteins. These findings support lactate shuttling as a mechanism for supplying lactate-derived carbon to mitochondrial energy metabolism, which may improve neuronal resilience under Aβ-induced metabolic stress.NEW & NOTEWORTHY This study shows that lactate treatment attenuates Aβ-induced cell death in neuron-like cells and supports mitochondrial carbon metabolism. Glycolytic hypometabolism was observed in human AD brain transcriptome and Aβ-treated neuron-like cells. We confirmed that lactate replenished mitochondrial energetics, making neurons more resilient to neurotoxicity. Using 13C tracing and metabolic flux analysis, we found that lactate-derived carbon was incorporated into the TCA cycle and that lactate treatment was associated with restoration of Aβ-suppressed mitochondrial fluxes.

Humans↗

PubMed2026

13C Stable Isotope Tracing-Based MFA Reveals the Contribution of Glucose to Glycolytic and TCA Fluxes and Its Application in Depression Research.

Metabolomics is widely applied to dissect metabolic pathways and their correlations with biological phenotypes. Unlike genomics and proteomics, metabolites exhibit substantial heterogeneity in chemical structure, physicochemical properties, and biological origin. Accordingly, pathway enrichment and annotation relying merely on alterations in metabolite abundance are prone to incomplete coverage, ionization bias, and ambiguous annotation, which inevitably impair the accuracy of pathway interpretation. Metabolic flux analysis (MFA) coupled with stable isotope-resolved metabolomics (SIRM) offers a powerful quantitative framework for tracing in vivo carbon flow and estimating reaction fluxes across key metabolic nodes. Glucose metabolism lies at the core of systemic energy homeostasis; however, most current investigations are confined to cell lines or in vitro systems, and a simple, easy-to-implement computational pipeline for in vivo glucose flux analysis in animal models is still lacking. Herein, we established an in vivo 13C-labeling-based MFA workflow to trace and resolve the systemic metabolic fate of glucose in rats. The pipeline covers tracer administration, sample preparation, LC-MS detection, isotopologue data acquisition and correction, construction of a glucose-metabolism-related metabolite database, MFA model establishment, and metabolic flux quantification. By infusing rats with [U-13C6]-glucose and [U-13C3]-sodium L-lactate, we precisely characterized the in vivo metabolic fates of circulating glucose and lactate and quantified their respective contributions to glycolytic flux and tricarboxylic acid (TCA) cycle flux. We further applied this workflow to profile energy metabolic reprogramming in depression. The results revealed a systemic shift toward aerobic glycolysis in rats exposed to chronic unpredictable mild stress (CUMS). Overall, the expanded application of this MFA strategy can provide mechanistic and quantitative insights into the regulation of metabolic pathways.

Animals↗

PubMed2026

The insulin-like growth factor 2 mRNA-binding protein 2 affects tumor cell metabolism via mitochondrial transporter activity and lipid alterations.

The insulin-like growth factor 2 mRNA-binding protein (IGF2BP) family is overexpressed in cancer and associated with poor prognosis. IGF2BP2 has been linked to single metabolic alterations by acting on its RNA targets. Here, we used a comprehensive approach to elucidate the effects of IGF2BP2 on primary and lipid metabolism. 13C-metabolic flux analysis (MFA) combined with RNA-Seq data revealed that IGF2BP2 affects mitochondrial fluxes by regulating the expression of several mitochondrial transporters, such as mitochondrial pyruvate carrier 1 (MPC1) and uncoupling protein 2 (UCP2). Methyl pyruvate reversed the gene expression patterns of UCP2 and CPT1A in HCT116 IGF2BP2 knockout (KO) cells by bypassing MPC1. Interestingly, an altered expression of the transporter UCP2 was also observed in a patient-derived tumor organoid (PDO), in which IGF2BP2 was knocked down. The altered glutamine metabolism seen in the 13C-MFA and the citrate label data derived from extracted mitochondria confirm a rerouting of glutamine almost exclusively into the mitochondria and a reduction of glycolytic carbon intake into the mitochondria. Due to changes in palmitate labeling patterns, lipid stainings were performed, suggesting lipid accumulation in KO cells. A lipidomic analysis revealed altered compositions across almost all lipid species. Further, lipogenic genes involved in fatty acid and cholesterol metabolism were differentially expressed. Most of the differentially expressed genes are potential direct targets of IGF2BP2 based on publicly available IGF2BP2 CLIP data. Overall, these results show the influence of IGF2BP2 on the central carbon metabolism of cancer cells, primarily through its effects on MPC1 and the resulting effects on UCP2. The complex interaction of IGF2BP2 with the metabolic network provides important insights into tumor metabolism, particularly relevant to tumor growth and resistance to therapy.

Journal Article↗

PubMed2026

CRISPR-Enabled functional genomics in hPSCs-derived neural models for autism spectrum disorder.

Autism Spectrum Disorder (ASD) is a genetically heterogeneous neurodevelopmental condition in which hundreds of individually rare risk variants converge on a small number of shared biological pathways, including synaptic scaffolding, chromatin remodeling, excitation-inhibition balance, and cellular energy metabolism. Translating this genetic heterogeneity into mechanistic insight requires experimental systems capable of interrogating individual gene functions in human-relevant neural contexts at scale. CRISPR-enabled functional genomics in human pluripotent stem cell (hPSC)-derived neural models, spanning neural progenitors, cortical and inhibitory neurons, astrocytes, microglia, and brain organoids, provides precisely this capability. By integrating pooled perturbation screens with multimodal readouts including single-cell and spatial transcriptomics, chromatin accessibility profiling, proximity labeling proteomics, multi-electrode array electrophysiology, and metabolic flux analysis, these platforms enable systematic, causal mapping of ASD gene function at system resolution. Early applications have already revealed convergent mechanisms: BAF complex disruption expands the ventral progenitor pool and biases its fate toward oligodendrocyte and interneuron lineages; ADNP loss impairs microglial synaptic pruning through altered endocytic trafficking; and mTOR pathway dysregulation in PTEN- and TSC2-perturbed models links genetic risk directly to metabolic and mitochondrial dysfunction. Computational frameworks including MIMOSCA and SCEPTRE enable causal network reconstruction and pseudotime inference from these datasets, moving the field from gene lists toward pathway-level models of ASD pathobiology. Translational applications leverage isogenic iPSC panels and variant-level base and prime editing to stratify ASD variants by functional impact, informing gene therapy design for haploinsufficient targets such as CHD8 and SCN2A via AAV or antisense oligonucleotide delivery. Remaining challenges, including model developmental immaturity, batch variability, and the difficulty of modeling polygenic risk, are addressed by a roadmap integrating spatial perturbomics, AI-driven causal inference, and population-scale standardized biobanks. This review synthesizes the current state of CRISPR-based functional genomics in human stem cell neural models as a coherent experimental framework for converting ASD genetic associations into mechanistic understanding and therapeutic opportunity.

Humans↗

PubMed2026

Engineering Bacillus Subtilis for Efficient Biosynthesis of Riboflavin: Current Knowledge and Future Perspectives.

Riboflavin is an essential water-soluble vitamin that serves as a precursor for the biosynthesis of the flavin cofactors FMN and FAD, which play pivotal roles in numerous redox and energy metabolism reactions. With the growing global demand for sustainable vitamin production, microbial fermentation has become an attractive alternative to chemical synthesis due to its environmental and economic advantages. Among microbial hosts, Bacillus subtilis has emerged as a leading cell factory for riboflavin production owing to its GRAS status, well-characterized genetics, and efficient protein secretion system. This review provides a comprehensive overview of recent advances in metabolic engineering strategies to enhance riboflavin biosynthesis in B. subtilis. Key topics include strengthening biosynthetic and precursor pathways, relieving feedback inhibition, balancing metabolic flux and cell growth, employing adaptive laboratory evolution, and utilizing omics-guided optimization and 13C metabolic flux analysis. Moreover, the integration of synthetic biology tools such as riboswitch engineering, regulatory element design, and high-throughput screening has significantly accelerated strain improvement. Despite remarkable progress, challenges remain in achieving precise regulatory control, optimizing multi-gene expression, and enhancing genome integration efficiency. Future research combining multi-omics data, synthetic regulatory design, and machine learning-driven predictive modeling is expected to further advance the development of intelligent B. subtilis cell factories. However, the practical implementation of these systems remains constrained by the metabolic burden of overproduction and the lack of universal regulatory models that can predict strain performance across varying industrial scales.

Bacillus subtilis↗

PubMed2026

Glucosamine links hyperglycemia to mTORC1 activation and glucose toxicity in diabetes.

Hyperglycemia is a principal driver of β cell failure and multiple-organ complications in diabetes. Chronic exposure to hyperglycemia overstimulates mTORC1, disrupting glucose metabolism and promoting ER stress, oxidative stress, and inflammation; however, the upstream metabolic signal(s) linking glucose to mTORC1 activation remains unclear. Here, we identified glucosamine as a key metabolite connecting elevated glucose to mTORC1 signaling in pancreatic islets and kidney, both major targets of hyperglycemic damage. Using 13C6-glucose metabolic labeling in diabetic rodents treated with or without the SGLT2 inhibitor dapagliflozin or insulin, combined with targeted metabolomics and metabolic flux analysis, we found that tissue glucose concentrations strongly correlated with glucosamine. A similar correlation with plasma glucose was conserved in humans with or without type 2 diabetes, and inversely associated with β cell function. In vitro, low-dose glucosamine stimulated mTORC1 in islets and kidney proximal tubule cells in an O-GlcNAcylation-dependent manner. Broad phosphoproteomics and transcriptomics analyses in β cells showed that glucosamine activated mTORC1-regulating pathways, induced oxidative stress, ER stress, and dedifferentiation. Genetic inhibition of β cell mTORC1 via heterozygous Raptor knockout, as well as pharmacologic inhibition of the glucosamine/mTORC1 axis through SGLT2 inhibition, alleviated β cell stress, improved glycemic control, and restored β cell function. These findings identified the glucosamine/mTORC1 pathway as an important mediator of β cell and kidney dysfunction in diabetes.

Animals↗

PubMed2025

Flux-sum coupling analysis of metabolic network models.

Metabolites acting as substrates and regulators of all biochemical reactions play an important role in maintaining the functionality of cellular metabolism. Despite advances in the constraint-based framework for genome-scale metabolic modeling, we lack reliable proxies for metabolite concentrations that can be efficiently determined and that allow us to investigate the relationship between metabolite concentrations in specific metabolic states in the absence of measurements. Here, we introduce a constraint-based approach, the flux-sum coupling analysis (FSCA), which facilitates the study of the interdependencies between metabolite concentrations by determining coupling relationships based on the flux-sum of metabolites. Application of FSCA on metabolic models of Escherichia coli, Saccharomyces cerevisiae, and Arabidopsis thaliana showed that the three coupling relationships are present in all models and pinpointed similarities in coupled metabolite pairs. Using the available concentration measurements of E. coli metabolites, we demonstrated that the coupling relationships identified by FSCA can capture the qualitative associations between metabolite concentrations and that flux-sum is a reliable proxy for metabolite concentration. Therefore, FSCA provides a novel tool for exploring and understanding the intricate interdependencies between the metabolite concentrations, advancing the understanding of metabolic regulation, and improving flux-centered systems biology approaches.

Escherichia coli↗

PubMed2016

Tracer-Based Metabolic NMR-Based Flux Analysis in a Leukaemia Cell Line.

High levels of reactive oxygen species (ROS) have a profound impact on acute myeloid leukaemia cells and can be used to specifically target these cells with novel therapies. We have previously shown how the combination of two redeployed drugs, the contraceptive steroid medroxyprogesterone and the lipid-regulating drug bezafibrate exert anti-leukaemic effects by producing ROS. Here we report a 13C-tracer-based NMR metabolic study to understand how these drugs work in K562 leukaemia cells. Our study shows that [1,2-13C]glucose is incorporated into ribose sugars, indicating activity in oxidative and non-oxidative pentose phosphate pathways alongside lactate production. There is little label incorporation into the tricarboxylic acid cycle from glucose, but much greater incorporation arises from the use of [3-13C]glutamine. The combined medroxyprogesterone and bezafibrate treatment decreases label incorporation from both glucose and glutamine into α-ketoglutarate and increased that for succinate, which is consistent with ROS-mediated conversion of α-ketoglutarate to succinate. Most interestingly, this combined treatment drastically reduced the production of several pyrimidine synthesis intermediates.

NMR spectroscopy↗

PubMed2026

Rational design of high-productivity perfusion processes for CHO Cells: From growth inhibitory strategies to model-driven optimization.

While perfusion culture for Chinese hamster ovary (CHO) cells offers advantages such as continuous operation and flexibility, it suffers from product loss through cell bleeding and difficulties in reaching high productivity due to sustained rapid cell growth. Growth inhibitory strategies are widely used to enhance productivity in fed‑batch processes; however, their practical implementation and comparative effectiveness in perfusion processes remain insufficiently explored. Meanwhile, process development often relies on costly trial‑and‑error approaches. Here, we systematically compared three growth inhibitory strategies in perfusion culture-low cell‑specific perfusion rate (CSPR), sodium butyrate, and mild hypothermia-with respect to cell growth, metabolism, productivity, and product quality. Genome‑scale metabolic flux sampling analysis revealed that low‑CSPR and sodium butyrate induce a convergent up‑regulation of energy metabolism, correlating with greater gains in specific productivity (qp). Building on this insight, we developed a growth‑kinetic model for the combined low‑CSPR + butyrate strategy, incorporating parameter uncertainty. This model‑guided framework enabled the rational design of two distinct high‑productivity perfusion processes: a sustained mode that achieved robust long‑term stability alongside substantial productivity gains, and a high‑intensity mode that pushed qp and daily volumetric titer to their maxima, with increases of up to 108.94% and 190.36%, respectively, in a model CHO cell line with a moderate baseline productivity. Our study provides a proof‑of‑concept framework for perfusion intensification, from strategy selection to rational process design.

Animals↗

PubMed2026

Combined high-fat, high-sucrose diet and streptozotocin treatment induces cardiometabolic heart failure with preserved ejection fraction in mice.

Diabetes is associated with an increased incidence of heart failure with preserved ejection fraction (HFpEF), but the underlying mechanisms are poorly understood. A shortage of mouse models reflecting the diverse HFpEF pathophysiology contributes to this inadequate understanding of disease mechanisms. We conducted a comprehensive analysis of a nongenetic, inducible type 2 diabetes mellitus (T2DM) mouse model about its suitability as a preclinical model of cardiometabolic, diabetes-induced HFpEF. T2DM was induced in C57Bl/6 mice by a high-fat/high-sucrose diet and a low-dose streptozotocin (DIO-STZ). Cardiac function was assessed in vivo by echocardiography and left ventricular catheterization and in vitro using the isolated perfused heart. Structural, molecular, and bioenergetic disturbances were analyzed by immunohistochemistry, RNA-seq, qPCR, Western blot, and extracellular flux analysis of myocardial tissue. Blood glucose, fatty acids, and ketone body levels were elevated, and insulin levels were reduced in DIO-STZ compared with chow. DIO-STZ mice showed an HFpEF phenotype with reduced cardiac output, end-diastolic volume, and increased filling pressure. No differences in myocardial fibrosis or in vitro stiffness were detected between DIO-STZ and chow. RNA-Seq pointed toward disturbances in lipid and ketone metabolism. Extracellular flux analysis revealed increased fatty acid oxidation capacity without differences in glucose metabolism. No general mitochondrial dysfunction was observed, but a reduced capacity for β-hydroxybutyrate oxidation. The diabetic DIO-STZ mouse model showed a pronounced functional HFpEF phenotype with underlying mechanisms that remarkably differ from other HFpEF models, making the DIO-STZ model a relevant extension of the range of HFpEF mouse models, especially for investigating molecular mechanisms or therapeutic interventions in diabetes-associated HFpEF.NEW & NOTEWORTHY Heart failure with preserved ejection fraction (HFpEF) is a clinical syndrome whose pathophysiological mechanisms are incompletely understood, potentially due to a lack of preclinical models reflecting the broad range of pathophysiological aspects. We describe a diabetic DIO-STZ mouse model showing a pronounced HFpEF with underlying mechanisms that remarkably differ from other HFpEF models, making this model a relevant extension of the range of HFpEF models, especially for investigating molecular mechanisms or therapeutical interventions in diabetes.

Animals↗

PubMed2025

HUMESS: integrating quantitative transcriptomic analysis and metabolic modeling to unveil condition-specific gene signatures.

SUMMARY: Transcriptomic analysis is a key tool for exploring gene expression, but the complexity of biological systems often limits its insights. In particular, the lack of intermodal or multi-layered analysis hinders the ability to fully capture key cellular functions such as metabolism from transcriptomic data alone. Here, we introduce a novel approach that informs transcriptomic data analysis with metabolic network modeling to address this. Unlike traditional methods, HUman MEtabolism Specific Signature (HUMESS) uses genome-scale metabolic modeling and flux analysis to highlight reactions and involved genes based on their metabolic significance, offering a deeper understanding of transcriptomic data. Our computational pipeline, supported by a user-friendly Rshiny application, enhances gene expression analysis by uncovering metabolic phenotypic signatures. AVAILABILITY AND IMPLEMENTATION: HUMESS is open source and available under GitLab https://gitlab.univ-nantes.fr/bird_pipeline_registry/humess with the complete documentation available at https://gitlab.univ-nantes.fr/bird_pipeline_registry/humess/-/wikis/Home. A zenodo archive is also available at the following DOI: https://doi.org/10.5281/zenodo.15487717. An RShiny application has been developed to facilitate the exploration and analysis of HUMESS's results. The app is available online at the following address: https://shiny-bird.univ-nantes.fr/app/shinymess but can also be installed locally, available under GitLab https://gitlab.univ-nantes.fr/pare-l/shinymess.

Humans↗

PubMed2026

NAViFluX: a visualization‑centric platform for interactive analysis, refinement and design of genome‑scale metabolic networks.

MOTIVATION: Genome-scale metabolic network (GSMN) models enable flux-based metabolite fate discovery, metabolic engineering, drug target identification, and multi-omics integration. However, programming requirements, architectural complexity, and limited visualization support impede its adoption by the broader scientific community. Existing tools exclusively specialize in GSMN analyses or visualization while lacking important features such as pathway-specific views, database-integrated refinement, and comprehensive enrichment and perturbation analyses. RESULTS: Here, we present NAViFluX (metabolic Network Analysis and Visualization of Flux), a visualization-centric, web browser-based tool that unifies native pathway/subsystem map generation, interactive model refinement via KEGG/BiGG, pathway merging and modules for flux computations, topology, and functional enrichment all within network views. Using three independent case studies on Escherichia coli, the utility of NAViFluX for characterization of nutrient-specific metabolic adaptations, enhancing gene essentiality predictions and interpretability, and rational design of an optimized carbon-fixing metabolic state is demonstrated. AVAILABILITY AND IMPLEMENTATION: All source code and supplementary files associated with the case studies are publicly available via Zenodo at https://zenodo.org/records/19107831. NAViFluX can be easily installed as a standalone software through https://github.com/bnsb-lab-iith/NAViFluX.

Metabolic Networks and Pathways↗

PubMed2026

Dynamic metabolic modelling 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 modelling of metabolism, we provide a mechanistic, dynamic and generalizable starting point for bridging systems biology modelling with viral pathogenesis. This framework could be extended to broadly incorporate phage dynamics in microbial systems and ecosystems.

Humans↗

PubMed2026

Diet modulates cardiac metabolic stress during anthracycline treatment.

Diet is a modifiable determinant of cardiovascular risk and may influence tolerance to cancer therapies. The mechanisms by which specific dietary components affect cardiac metabolism during anthracycline treatment remain poorly defined, limiting the incorporation of dietary recommendations into treatment guidelines. Here, we integrated heart proteomics data from patients treated with or without anthracyclines with a genome-scale reconstruction of human cardiac metabolism (CardioNet). Using constraint-based flux analysis, we conducted >30,000 in silico simulations of diet scenarios generated from chemical profiles of ∼500 foods curated in the Periodic Table of Food Initiative. These simulations revealed that diets enriched in rapidly absorbable sugars and depleted of essential fatty acids impair cardiac metabolic efficiency, increasing reactive oxygen species production and the demand for purine salvage fluxes. These predicted metabolic patterns were consistent with plasma metabolomics from patients treated with anthracyclines, validating our findings. Computational modeling of 39 recipes across six cuisines revealed cardiometabolic effects of omnivorous versus vegan diets in patients. Modeling of a healthy vegan diet increased cardiometabolic efficiency compared with a healthy omnivorous diet in patients treated with anthracyclines, independent of the culinary background. Our approach demonstrates that integrating the molecular composition of food with genome-scale metabolic models enables systematic analysis of diet patterns for translational testing. Ultimately, these in silico studies provide a framework for trials and may inform dietary recommendations for improving cardiometabolic health.NEW & NOTEWORTHY We developed a systems biology framework to predict how diet influences cardiac metabolism during cancer therapy. Across >30,000 in silico diet simulations, we identified nutrient patterns that either exacerbate or mitigate anthracycline-induced metabolic stress. These findings demonstrate how computational modeling can uncover diet-metabolism interactions driving cardiotoxicity and guide dietary interventions.

Humans↗

PubMed1975

A quantitative analysis of metabolite fluxes along some of the pathways of intermediary metabolism in Tetrahymena pyriformis.

A detailed model of intermediary metabolism has been constructed which is consistent with all known information on the compartmental structure of metabolism in Tetrahymena, on the enzyme complement of this cell, and on the localization of the enzymes. The model allows computation of the specific activity of every carbon atom of all metabolites and thus of the flux of carbon along the major pathways of metabolism under steady state conditions. To test the model, data were required from cells grown under standard conditions and then suspended in a dilute salt solution and incubated for 1 hour in a mixture of acetate, pyruvate, hexanoate, bicarbonate, and glutamate labeled in a total of 10 positions, but with only one substrate labeled in any given flask. Twenty-seven measurements of label incorporation into CO2, lipids, glycogen, glutamate, and alanine were made, plus measurements of label distribution into fatty acid and glycerol moieties for 4 of the substrates and of oxygen consumption and of glycogenolysis, yielding 33 independent measurements. These, plus about 18 "limit" measurements which also constrain any possible solutions, were in sufficient excess of the 23 independent parameters to permit a stringent assessment of the model. Equations derived directly from the structure of the model and from the known stereochemistry of the reactions were programmed on a PDP-15 computer and values of the Qo2 and of label expected to be incorporated into the various products actually measured were computed for any given set of flux rates. A set of flux rates was found which yielded an excellent fit to the observed data. The ability to achieve a fit to the data for an overdetermined system constitutes strong support for this structural model of intermediary metabolism and the computed flux rates therefore provide a quantitative description of metabolite flow in the intact cell. Despite the redundancy of measurements relative to parameters to be determined, it was not possible to define a unique set of values for the flux through phosphoenolpyruvate carboxylase and phosphoenolpyruvate carboxykinase, although the relationship between these fluxes is specified by the model. The analysis allows estimation of the recycling of phosphoenopyruvate through pyruvate kinase under conditions of net glyconeogenesis and an apparently futile exchange of acetyl-CoA between the inner and outer mitochondrial compartments. Carbon flow through the glyoxylate bypass under these conditions is about one-third of that through the Krebs cycle. The analysis also shows a net transport of malate from the peroxisomes to the mitochondria, consistent with the anaplerotic role of the peroxisomal glyoxylate bypass in Tetrahymena.

Acetates↗

PubMed2026

BioEMMA: Automated Generation of Model-Specific Escher-Compatible Maps from KEGG Pathways.

Genome-scale metabolic models are widely used to investigate cellular metabolism, but their interpretation and comparison are limited by the lack of reproducible pathway-level visualizations with a common spatial organization. This study presents BioEMMA, a Python-based tool for the automated generation of model-specific metabolic pathway maps in the Escher JSON format using coordinate information from curated KEGG pathway maps. BioEMMA parses KGML files, map reaction and metabolite identifiers to model database namespaces, filters pathway elements according to an input SBML model, adds non-primary metabolites, reconstructs Escher-compatible layouts, and supports flux visualization. The tool was integrated into a reproducible BioUML workflow for metabolic model reconstruction. BioEMMA was evaluated using the e_coli_core model and the KEGG glycolysis/gluconeogenesis pathway while generating a model-specific map with overlaid FBA fluxes. It was then applied to compare E. coli reconstructions generated by gapseq, ModelSEEDpy, and Reconstructor across three central carbon metabolism pathways. To broaden the evaluation, BioEMMA was applied using 87 prokaryotic BiGG models and three eukaryotic models. The analysis revealed pathway-specific differences in reaction coverage, shared and model-specific reactions, and predicted flux activity. BioEMMA therefore provides a reproducible framework for pathway-level visualization and comparison of genome-scale metabolic reconstructions within a common spatial coordinate system.

Escher maps↗

PubMed2026

Membrane and proteome allocation constraints in Escherichia coli models during overflow metabolism.

The allocation of finite cellular resources is a fundamental principle that dictates microbial metabolic strategies and gives rise to complex phenomena, such as overflow metabolism, characterized by the production of respiro-fermentative by-products, including acetate, during rapid growth. Although proteome-constrained models have successfully predicted overflow metabolism in Escherichia coli, they often overlook the distinct biophysical and energetic costs associated with protein localization. The cellular membrane, in particular, represents a critical and constrained compartment where competition for space and synthesis machinery can create significant metabolic bottlenecks. To investigate this, we developed the membrane-associated constrained flux balance analysis (MAFBA), a scalable, genome-scale metabolic model that introduces a tunable constraint on the total protein mass allocated to the cellular membrane. Our model demonstrates that the overall and membrane-associated proteome allocation constraints interact to improve the accuracy of predicting the onset of overflow metabolism. It mechanistically reveals that at high growth rates, competition for limited membrane allocation forces a trade-off between growth-essential functions and respiratory capacity, leading to acetate production. Furthermore, MAFBA quantitatively explains the widely observed experimental phenomenon that expressing heterologous membrane proteins imposes a significantly higher metabolic burden than expressing cytosolic proteins. This study establishes membrane resource allocation as a key constraint governing bacterial physiology, acting in concert with overall proteome limitations. The resulting MAFBA framework provides a powerful and accessible tool for synthetic biology and metabolic engineering, enabling the prediction of metabolic costs associated with expressing membrane-bound proteins and guiding strain design strategies, holding promise for applications in bioproduction and metabolic engineering.

Escherichia coli↗