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At least 19 recordsLinked to original sources

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

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

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

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

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

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

Identification and analysis of metabolic reprogramming-related genes in triple-negative breast cancer.

Triple-negative breast cancer (TNBC) is notorious for its rapid progression, tendency to metastasize, high recurrence rates, dismal outcomes, and limited treatment options, underscoring the urgent need to uncover new biomarkers and molecular pathways to enhance diagnosis, prognosis, and therapeutic strategies. Metabolic reprogramming continues to play a role throughout the life cycle of cancer, evolving and adapting. In this study, we aimed to identify specific genes associated with metabolic reprogramming in TNBC, which can potentially become unique biomarkers of this cancer. TNBC datasets retrieved from the Gene Expression Omnibus were employed to pinpoint genes exhibiting altered expression linked to tumor metabolic reprogramming. Key genes were accurately screened through machine learning algorithms, and then externally verified using the TBNC dataset based on the Cancer Genome Atlas database. Finally, immunohistochemical methods were used to clinically confirm the differential expression and trends of these key genes. Our analysis accurately identified four genes-CLEC7A, IRS1, RSPO3, and ALB-that are closely correlated with the metabolic reprogramming characteristics of cancer, and could be regarded as innovative biomarkers for TNBC. This opens a new avenue for further investigation into the mechanisms of metabolic reprogramming in TNBC and new treatment strategies.

Humans

Superiority of interconvertible enzyme cascades in metabolic regulation: analysis of monocyclic systems.

A theoretical analysis of monocyclic cascade models shows that the steady-state fraction of covalently modified interconvertible enzyme is a function of 10 different cascade parameters. Because each parameter can be varied independently, or several can be varied simultaneously, by single or multiple allosteric interactions of ligands with one or more of the cascade enzymes, interconvertible enzymes are exquisitely designed for the rigorous control of key metabolic steps. Compared with other reglatory enzymes, they can respond to a greater number of allosteric stimuli, they exhibit greater flexibility in overall control patterns, and they can generate a greatly amplified response to primary allosteric interactions of effectors with the converter enzymes. Contrary to earlier views, the decomposition of ATP associated with cyclic coupling of the covalent modification and demodification reactions is not a futile process. ATP decomposition supplies the energy needed to maintain concentrations of modified enzyme at steady-state levels that are in excess of those obtainable at true thermodynamic equilibrium.

Adenosine Triphosphate

Adolescent depression as a systemic multimorbidity catalyst: integrated genetic and metabolic pathway analysis.

BACKGROUND: Although adolescent depression has been linked to individual chronic conditions, its broader role in shaping multimorbidity risk remains understudied. METHODS: A total of 87,562 UK Biobank participants were included, of whom 18,851 had documented adolescent depression. Cox proportional hazards models were applied to evaluate associations between adolescent depression and 24 chronic diseases, followed by stratified analyses by sex and age. Two-sample Mendelian randomization (MR) was then conducted to infer causality for diseases showing significant associations. Genomic colocalization analyses were performed using relevant GWAS data to identify shared causal variants. Mediation analyses were performed to detect possible mediating factors, including the frailty index, KDM biological age acceleration, allostatic load and 30 circulating biomarkers. RESULTS: Adolescent depression was associated with elevated risk for 12 chronic diseases, with strongest associations for hypothyroidism (HR = 1.29 [1.18-1.42]), diabetes (HR = 1.25 [1.13-1.38]) and chronic obstructive pulmonary disease (COPD) (HR = 1.74 [1.50-2.01]). Risks were notably higher among females and younger adults. MR confirmed likely causal relationships for hypothyroidism (OR = 1.45 [1.03-2.05]), diabetes (OR = 1.01 [1.01-1.02]) and COPD (OR = 1.04 [1.02-1.06]). Genomic colocalization revealed a shared genetic signal at the CDSN/PSORS1C1 locus between adolescent depression and hypothyroidism. Mediation analyses revealed disease-specific pathways: creatinine for hypothyroidism, testosterone for diabetes, KDM biological ageing for COPD and frailty index across all three conditions. CONCLUSIONS: Adolescent depression confers systemic vulnerability through genetic and metabolic mechanisms, with amplified risks in females and individuals aged ≤55 years. These findings support early, integrated interventions to mitigate long-term multimorbidity.

Humans

Gas-lipuid chromatographic analysis of metabolic products in the identification of bacteroidaceae of clinical interest.

The acid end-products of 185 isolates from the family Bacteroidaceae were separated and analysed by gas-liquid chromatography on broth cultures. Different media were evaluated and definitive studies were performed in a fully supplemented complex medium. The limitations of this approach to the identification of a wide range of strains from various clinical sources were determined and the results were compared with those of a series of morphological, biochemical, tolerance and antibiotic-resistance tests. All test strains were identified to generic level by simple microscopic and colonial observations and GLC analysis; additional tests were required to allow species or subspecies identification of most strains. Population differences were detected between some species or subspecies isolated from different clinical sites by quantitative analyses of fatty acids, but individual strains could not always be separated because of overlapping ranges of distribution of acids that were common products of more than one species or subspecies. Small differences in minor products between different species or subspecies were variable and are not considered adequate for discrimination at these taxonomic levels without support from other observations. The potential application of the GLC technique to the rapid and accurate identification of these organisms in hospital laboratories is considered.

Bacteroidaceae