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At least 19 recordsLinked to original sources
PubMed1992

[Noninvasive metabolic analysis of the diabetic cornea and lens: in vivo measurement].

In vivo measurement of metabolic changes in diabetic cornea and lens were performed using redox fluorometry in nonobese diabetic (NOD) mice. Autofluorescence from reduced pyridine nucleotides (PN) and oxidized flavoproteins (Fp) were measured, and the PN/Fp ratio was used as a tissue metabolism indicator. The PN/Fp ratios were significantly higher in the diabetic corneal endothelium. Morphometric analysis of the corneal endothelium using specular microscopy revealed no significant differences between the two groups. These results indicate that redox fluorometry is able to detect early metabolic changes in the corneal endothelium and lens epithelium, which are induced by diabetes mellitus. Activation of the polyol pathway may be responsible for the change. Corneal epithelia may be less susceptible to diabetic changes than the corneal endothelium and lens epithelium.

Animals↗
PubMed1992

[Metabolic analysis of the diseased human corneal endothelium].

Redox states of the corneal endothelium in 42 recipient corneas obtained at the penetrating keratoplasty were measured non-invasively using ocular redox fluorometry. Autofluorescence from reduced pyridine nucleotides (PN) and oxidized flavoproteins (Fp) were measured, and the PN/Fp ratio was used as an indicator of the redox state. Endothelial damage was graded as normal, mildly damaged, moderately damaged, and severely damaged, based on the histopathological findings. Mildly damaged endothelium showed a significantly higher PN/Fp ratio than the that in normal endothelium whereas the ratio was significantly lower in the severely damaged endothelium. These changes in the redox state may represent compensation and decompensation processes of the endothelial metabolism. Ocular redox fluorometry was shown to be useful for the evaluation of the metabolic state in the human corneal endothelium.

Adult↗
PubMed1975

Human islet cell adenoma: metabolic analysis of the patient and of tumor cells in monolayer culture.

Cell cultures were established from a benign pancreatic islet adenoma. Over 200 muU/culture/day immunoreactive insulin were found in culture media. Cultures with medium 199 released insulin for about 2 months; those with medium F12K were maintained for over 7 months, and have been successfully subcultured. Increasing culture medium glucose to 326 mg per 100 ml, alone or with leucine (10 mM) or theophylline (2 mM), failed to increase insulin release above baseline. Studies in the patient prior to surgery using oral glucose, leucine, beef meal, intravenous tolbutamide, and glucagon failed to increase plasma insulin and thus were consistent with cell culture responses. Extracts of tumor tissue contained 23% proinsulin-like material; high insulin containing samples of culture medium had 5% proinsulin and less than 40 pg glucagon/ml. Aldehyde fuchsin positive granulation was sparse in both cultured cells and the original tumor. These studies demonstrate long term viability, in monolayer culture, of cells derived from this islet cell adenoma, with retention of secretory characteristics consistent with data obtained prior to removal of the adenoma from the patient.

Adenoma, Islet Cell↗
PubMed1992

The definitions of metabolic control analysis revisited.

Various definitions of coefficients in metabolic control analysis are examined with respect to their theoretical consistency and practical applicability. We suggest agreement upon a definition for control coefficients which is clearly distinct from that for response coefficients, in such a way that the former describe inherent properties of the metabolic system while the latter refer to the influence of special parameters. Advantages and drawbacks of using normalized or non-normalized control coefficients are studied. It is shown that normalized control coefficients have the advantage of being invariant to a different rescaling of the particular fluxes. We demonstrate that some problems are easier to tackle if the consistency of time-independent control coefficients with their time-dependent counterparts is taken into account. It is shown that the matrix of flux control coefficients is an indempotent matrix. This allows an interpretation in terms of the transduction of the effect of parameter perturbations. Several aspects of the experimental measurement of control coefficients are discussed, with special reference to the different definitions.

Enzymes↗
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↗
PubMed1992

Characterisation of the control of respiration in potato tuber mitochondria using the top-down approach of metabolic control analysis.

Control over oxidative phosphorylation by purified potato mitochondria was determined using the top-down approach of metabolic control analysis. The control over the respiration rate, phosphorylation rate, proton-leak rate and proton motive force exerted by the respiratory chain, phosphorylation reactions and the proton leak were measured over a range of phosphorylation rates from resting (state 4) to maximal (state 3). These rates were obtained by adding different amounts of hexokinase in the presence of glucose, or different amounts of oligomycin in the presence of ADP. The respiratory substrate was NADH or succinate, both of which feed electrons directly to ubiquinone. The rate of oxygen consumption by the alternative oxidase pathway was negligible with NADH as substrate but was measurable with succinate and was subtracted. Control over the respiration rate in potato mitochondria was predominantly exerted by the respiratory chain at all rates except close to state 4, where control by the proton leak was equally or more important. For oxidation of NADH, the flux control coefficient over the respiration rate exerted by the respiratory chain in state 3 was between 0.8 and 1.0, while in state 4, control over the respiration rate was shared about equally between the chain and the proton leak. The control over the phosphorylation rate was predominantly exerted by the respiratory chain, although at low rates control by the phosphorylation system was also important. For oxidation of NADH, the flux control coefficient over the phosphorylation rate exerted by the respiratory chain in state 3 was 0.8-1.0, while near state 4 the flux control coefficients over the phosphorylation rate were about 0.8 for the phosphorylation system and 0.25 for the chain. Control over the proton leak rate was shared between the respiratory chain and the proton leak; the phosphorylation system had negative control. For oxidation of NADH, the flux control coefficients over the leak rate in state 3 were 1.0 for the leak, 0.4 for the chain and -0.4 for the phosphorylation system, while in state 4 the flux control coefficients over leak rate were about 0.5 for the leak and 0.5 for the chain. Control over the magnitude of the protonmotive force was small, between -0.2 and +0.2, reflecting the way the system operates to keep the protonmotive force fairly constant; the respiratory chain and the phosphorylation system had equal and opposite control and there was very little control by the proton leak except near state 4.

Hydrogen-Ion Concentration↗
PubMed1977

Analysis of metabolic profiles of bile acids in urine using a lipophilic anion exchanger and computerized gas-liquid chromatorgaphy-mass spectrometry.

A method is described for quantitative analysis of bile acids in urine. Urine is acidified and bile acids are extracted on an Amberlite XAD-2 column. Bile salts are converted to acids on an Amberlyst A-15 column and are separated into groups of unconjugated, glycine, taurine, monosulfated, and polysulfated conjugates using the lipophilic anion exchanger diethylaminohydroxypropyl Sephadex LH-20 (DEAP-LH-20). After solvolysis and hydrolysis, the deconjugated bile acids are purified on DEAP-LH-20, and are converted to methyl ester trimethylsilyl ether derivatives. Identification and quantitation of the individual bile acids is accomplished by computerized gas-liquid chromatography-mass spectrometry. The daily excretion of bile acids in urine from healthy subjects was 6.4-11 micro moles. The mixture of bile acids was quite complex and differed from that in bile. About 30 bile acids were identified or partially characterized. Three of these were monosubstituted: lithocholic, allolithocholic, and 3beta-hydroxy-5-cholenoic acids. Fourteen disubstituted bile acids included epimers of deoxycholic, allodeoxycholic, chenodeoxycholic, allochenodeoxycholic, and hyodeoxycholic acids. 3alpha-Hydroxy-12-keto-5beta-cholanoic acid was the major ketonic bile acid and 3beta,12alpha-dihydroxy-5-cholenoic acid was the major unsaturated bile acid in this group. Nine trihydroxy bile acids included cholic and allocholic acids, epimers of these compounds, hyocholic acid, and a 1-hydroxylated bile acid tentatively characterized as 1,3,12-trihydroxycholanoic acid. Cholestatic subjects excreted tetrahydroxycholanoates carrying hydroxyl groups in positions 1, 3, 6, 7, 12, or 23. All monohydroxy and the predominant part of dihydroxy bile acids were present in the monosulfate fraction. Exceptions were 3alpha,12beta-dihydroxy- and 3alpha-hydroxy-12-keto-5beta-cholanoic acids, which were found mainly in the glycine conjugate fraction. Most of the trihydroxy bile acids were nonsulfated, and cholic and norcholic acids were the major unconjugated bile acids. The tetrahydroxy bile acids and hyocholic acid were present mainly in the taurine conjugate fraction, while 1,3,12-trihydroxycholanoic acid was predominantly found in the glycine conjugate fraction. Sulfation of trihydroxy bile acids was increased in patients with marked cholestasis. All bile acids in the monosulfate fraction were conjugated and carried the sulfate ester group at C-3. Significant amounts of di- and trisulfates were not found. The results indicate selective mechanisms for sulfation, hydroxylation, and renal elimination of bile acid conjugates. Analysis of metabolic profiles of bile acids in urine may be a useful method in studies of the function of organs involved in bile acid metabolism.

Bile Acids and Salts↗
PubMed2025

MiNEApy: enhancing enrichment network analysis in metabolic networks.

MOTIVATION: Modeling genome-scale metabolic networks (GEMs) helps understand metabolic fluxes in cells at a specific state under defined environmental conditions or perturbations. Elementary flux modes (EFMs) are powerful tools for simplifying complex metabolic networks into smaller, more manageable pathways. However, the enumeration of all EFMs, especially within GEMs, poses significant challenges due to computational complexity. Additionally, traditional EFM approaches often fail to capture essential aspects of metabolism, such as co-factor balancing and by-product generation. The previously developed Minimum Network Enrichment Analysis (MiNEA) method addresses these limitations by enumerating alternative minimal networks for given biomass building blocks and metabolic tasks. MiNEA facilitates a deeper understanding of metabolic task flexibility and context-specific metabolic routes by integrating condition-specific transcriptomics, proteomics, and metabolomics data. This approach offers significant improvements in the analysis of metabolic pathways, providing more comprehensive insights into cellular metabolism. RESULTS: Here, I present MiNEApy, a Python package reimplementation of MiNEA, which computes minimal networks and performs enrichment analysis. I demonstrate the application of MiNEApy on both a small-scale and a genome-scale model of the bacterium Escherichia coli, showcasing its ability to conduct minimal network enrichment analysis using minimal networks and context-specific data. AVAILABILITY AND IMPLEMENTATION: MiNEApy can be accessed at: https://github.com/vpandey-om/mineapy.

Metabolic Networks and Pathways↗
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↗
PubMed1976

On the role of enzyme cooperativity in metabolic oscillations: analysis of the Hill coefficient in a model for glycolytic periodicities.

The role of enzyme cooperativity in the mechanism of metabolic oscillations is analyzed in a concerted allosteric model for the phosphofructokinase reaction. This model of a dimer enzyme activated by the reaction product accounts quantitatively for glycolytic periodicities observed in yeast and muscle. The Hill coefficient characteristic of enzyme-substrate interactions is determined in the model, both at the steady state and in the course of sustained oscillations. Positive cooperativity is a prerequisite for periodic behavior. A necessary condition for oscillation in a dimer K system is a Hill coefficient larger than 1.6 at the unstable stationary state. The analysis suggests that positive as well as negative effectors of phosphofructokinase inhibit glycolytic oscillations by inducing a decrease in enzyme cooperativity. The results are discussed with respect to glycolytic and other metabolic periodicities.

Allosteric Regulation↗
PubMed2026

Glutamate metabolic correlation analysis reveals CnP5CS1 contributes to 2-acetyl-1-pyrroline accumulation in aromatic coconut.

Flavor quality, a key sensory attribute of coconut, has consistently been a central breeding objective throughout long-term domestication and varietal improvement efforts. Developing high-aroma varieties requires a clear understanding of their underlying molecular genetic mechanisms. However, research on the metabolic regulatory enzymes involved remains limited, particularly those linked to 2-acetyl-1-pyrroline (2AP), a volatile compound that primarily contributes to the unique scent of aromatic coconuts. We developed contrasting populations and systematically evaluated the role of CnP5CS in 2AP accumulation by examining enzyme activity, metabolic flux, population-level genetic variation, and transcriptional regulatory networks. In the aromatic coconut population, the selected genomic regions were enriched in pathways associated with amino acid metabolism and stress responses. Conspicuously, glutamate (Glu) and its derivatives showed significant correlations within the differentiated populations. The Glu metabolic enzyme P5CS was subjected to strong purifying selection, and haplotype-phenotype association analysis further identified the dominant CnP5CS1 allele genotype. Moreover, we established metabolic marker indicators to assess relative 2AP levels, based on the metabolic profiles of CnP5CS and the substrates and products of its catalyzed reactions. The Y1H assay identified the key transcription factor CnYAB2, which exhibited a strongly correlated expression pattern with CnP5CS1 and major markers of 2AP metabolism. The identification of CnP5CS1 offers a novel perspective on the genetic regulation of 2AP metabolism in aromatic coconuts and establishes a theoretical foundation for developing molecular markers to support the breeding of high-aroma varieties.

Aroma↗
PubMed1992

Metabolic control analysis using transient metabolite concentrations. Determination of metabolite concentration control coefficients.

The methodology previously developed for determining the Flux Control Coefficients [Delgado & Liao (1992) Biochem. J. 282, 919-927] is extended to the calculation of metabolite Concentration Control Coefficients. It is shown that the transient metabolite concentrations are related by a few algebraic equations, attributed to mass balance, stoichiometric constraints, quasi-equilibrium or quasi-steady states, and kinetic regulations. The coefficients in these relations can be estimated using linear regression, and can be used to calculate the Control Coefficients. The theoretical basis and two examples are discussed. Although the methodology is derived based on the linear approximation of enzyme kinetics, it yields reasonably good estimates of the Control Coefficients for systems with non-linear kinetics.

Enzymes↗