Metabolic Model Research Articles (Page 6)

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.

Characterization of corneal and retinal changes in a metabolic syndrome model in young and adult mice.

Kinetic-model-guided engineering of multiple S. cerevisiae strains improves p-coumaric acid production.

Proteome trade-off between primary and secondary metabolism shapes acid stress induced bacterial exopolysaccharide production.

Metabolic engineering in Hot Acid: Strategies enabling chemolithotrophy in thermoacidophilic archaea.

Engineering natural microbial communities: harnessing synthetic communities for bioremediation.

Technological advancement spurs Komagataella phaffii as a next-generation platform for sustainable biomanufacturing.

HEK293 cells are a versatile cell line extensively used in the production of recombinant proteins and viral vectors, notably Adeno-associated virus (AAV) (Bulcha et al., 2021). Despite their high transfection efficiency and adaptability to various culture conditions, challenges remain in achieving sufficient yields of active viral particles. This study presents a comprehensive multi-omics analysis of two HEK293 strains under good manufacturing practice conditions, focusing on the metabolic and cellular responses during AAV production. The investigation included lipidomic, exometabolomic, and transcriptomic profiling across different conditions and time points. Genome-scale metabolic models (GSMMs) were reconstructed for these strains to elucidate metabolic shifts and identify potential bottlenecks in AAV production. Notably, the study revealed significant differences between a High-producing (HP) and a Low-producing (LP) HEK293 strains, highlighting pseudohypoxia in the LP strain. Key findings include the identification of hypoxia-inducible factor 1-alpha (HIF-1α) as a critical regulator in the LP strain, linking pseudohypoxia to poor AAV productivity. Inhibition of HIF-1α resulted in immediate cessation of cell growth and a 2.5-fold increase in viral capsid production, albeit with a decreased number of viral genomes, impacting the full-to-empty particle ratio. This trade-off is significant because it highlights a key challenge in AAV production: achieving a balance between capsid assembly and genome packaging to optimize the yield of functional viral vectors. Overall this suggests that while HIF-1α inhibition enhances capsid assembly, it simultaneously hampers nucleotide synthesis via the pentose phosphate pathway (PPP), necessary for nucleotide synthesis, and therefore for AAV genome replication.

Improving metabolic engineering design with enzyme-thermo optimization.

Simulation of sildenafil metabolism using an electrochemical oxidation system.

Systems-level analysis provides insights on methanol-based production of l-glutamate and its decarboxylation product γ-aminobutyric acid by Bacillus methanolicus.

Nitrogen is an essential element in all organisms, and its availability and use efficiency directly impact organismal growth and performance, especially in plants. Aminotransferases are core enzymes of the nitrogen metabolic network for synthesizing various organonitrogen compounds. Although each aminotransferase can potentially catalyse hundreds of transamination reactions with different combinations of amino and keto acid substrates, the full functionality of many aminotransferases remains elusive. Here we employed high-throughput gene synthesis and enzyme assay platforms to determine the substrate specificities of 38 aminotransferases of Arabidopsis thaliana and unveiled many previously unrecognized activities among a total of 4,104 reactions tested. The integration of these biochemical data in an enzyme-constrained metabolic model of Arabidopsis and in silico simulation further revealed that the promiscuity of aminotransferases may alter nitrogen distribution profiles and contribute to the robustness of the nitrogen metabolic network. This study provides foundational knowledge for deciphering the plant nitrogen metabolic network and improving nitrogen use efficiency in crops.

Nucleotides are indispensable biomolecules, playing vital roles in genetic information transfer, energy metabolism, cofactor biosynthesis, and cellular communication. These compounds (including purine nucleotides, nucleosides, and nucleobases) have become increasingly valuable as foodstuff additives and pharmaceutical intermediates. Although microbial production offers an eco-friendly alternative, its efficiency remains constrained by complex metabolic networks and growth-production tradeoffs. Systems metabolic engineering has emerged as a powerful approach to optimize purine biosynthesis in microorganisms. This review provides a systematic synthesis of recent advances in microbial purine biosynthesis. First, a comprehensive analysis of purine biosynthetic pathways and their regulatory networks in industrial microorganisms are presented, along with a comparative evaluation of current metabolic engineering approaches. Second, systems metabolic engineering strategies for production enhancement are examined, focusing on multi-omics integration, metabolic flux analysis, genome-scale metabolic models, dynamic regulation, and high-throughput screening platforms. Finally, the major challenges confronting efficient microbial production of purine compounds are identified, with proposed strategies to overcome these limitations.

Modeling host-pathway dynamics at the genome scale with machine learning.

Integrating transcriptomic data with metabolic model unravels the electron transfer mechanisms of Methanosarcina barkeri

How to maintain long-term euglycemia in a noisy environment: Insight from a stochastic glucose–insulin metabolism model with correlated Gaussian colored noise

Improving poly(3-hydroxybutyrate-co-3-hydroxyvalerate) synthesis with the construction of a genome-scale metabolic model for Haloferax mediterranei.

Attention deficit hyperactivity disorder (ADHD) is a neurodevelopmental condition marked by hyperactivity, impulsivity, and inattentiveness that are disproportionate to the patient's developmental stage. Individuals with ADHD often experience gastrointestinal (GI) issues, indicating a potential link with the gut microbiome. This study aims to explore how various parameters influence the production and consumption of metabolites in the brain by developing an integrated gut-brain metabolic model, examining the impact of gut microbiota-derived metabolites on the human brain. Genome-scale metabolic models (GEMs), which consider gene-protein-reaction relationships, are utilized to simulate metabolic processes in gut microorganisms. A comprehensive genome-scale metabolic model of the human brain, comprising 812 metabolites, 994 reactions, 671 genes, and 71 metabolic pathways, serves as the healthy brain reference. To mimic an ADHD brain, the gene NOS1 is removed from the healthy model. An integrated gut-brain model is created using a three-compartment approach (gut, blood, and brain). This modeling technique, which accounts for microbial genome-environment interactions and their metabolite interactions with other human organs, helps identify the GI mechanisms underlying ADHD toward enhancing the quality of life for affected individuals. Moreover, understanding the relationship between ADHD, age, gender, and diet can help in developing more effective, personalized approaches to managing ADHD.

Programmed cell death protein-ligand 1 (PD-L1) expression is an important marker for immunotherapy in locally advanced non-small cell lung cancer (LA-NSCLC). PD-L1 expression has a bi-directional positive feedback relationship with glycolysis status. This study aimed to develop a metabolic habitat model based on 18F-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) images to predict PD-L1 expression levels in patients with LA-NSCLC, and to explore relevant biological characteristics. We included 219 patients from two independent centers and divided them into the training (n = 175) and testing (n = 44) cohorts. Tumors were segmented into four spatially distinct, biologically similar metabolic habitat subregions using the Otsu method. Radiomic characteristics and metabolic parameters were extracted from each habitat and used to generate multiple predictive models based on the Extra Trees classifier. Data from 1043 patients in The Cancer Genome Atlas database were used to analyze the genes associated with PD-L1 expression in NSCLC. The metabolic habitat model exhibited the highest performance, with area under the curve values of 0.833 and 0.786 in the training and testing cohorts, respectively, outperforming other models. Subregion analysis revealed that high-glycolytic/high-density habitats (PETHigh-CTHigh) exhibited the highest metabolic characteristics, and their spatial distribution correlated positively with PD-L1 expression. Four genes (IFNG, IL2RA, HK3, and MYCN) were associated with PD-L1 expression in glycolysis gene correlation analysis. The metabolic habitat model based on 18F-FDG PET/CT enables noninvasive prediction of PD-L1 expression in LA-NSCLC. Its interpretability is enhanced by spatial habitat distribution, thereby advancing its potential for clinical translation.

Microbial bellwether: Community-scale metabolic modeling to predict infection.