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Results for “Metabolic Networks and Pathways”

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PubMed2005

Comprehensive post-genomic data analysis approaches integrating biochemical pathway maps.

Post-genomic era research is focusing on studies to attribute functions to genes and their encoded proteins, and to describe the regulatory networks controlling metabolic, protein synthesis and signal transduction pathways. To facilitate the analysis of experiments using post-genomic technologies, new concepts for linking the vast amount of raw data to a biological context have to be developed. Visual representations of pathways help biologists to understand the complex relationships between components of metabolic networks, and provide an invaluable resource for the integration of transcriptomics, proteomics and metabolomics data sets. Besides providing an overview of currently available bioinformatic tools for plant scientists, we introduce BioPathAt, a newly developed visual interface that allows the knowledge-based analysis of genome-scale data by integrating biochemical pathway maps (BioPathAtMAPS module) with a manually scrutinized gene-function database (BioPathAtDB) for the model plant Arabidopsis thaliana. In addition, we discuss approaches for generating a biochemical pathway knowledge database for A. thaliana that includes, in addition to accurate annotation, condensed experimental information regarding in vitro and in vivo gene/protein function.

Computational Biology↗
PubMed2004

Stoichiometric network constraints on xylose metabolism by recombinant Saccharomyces cerevisiae.

Metabolic pathway engineering is constrained by the thermodynamic and stoichiometric feasibility of enzymatic activities of introduced genes. Engineering of xylose metabolism in Saccharomyces cerevisiae has focused on introducing genes for the initial xylose assimilation steps from Pichia stipitis, a xylose-fermenting yeast, into S. cerevisiae, a yeast traditionally used in ethanol production from hexose. However, recombinant S. cerevisiae created in several laboratories have used xylose oxidatively rather than in the fermentative manner that this yeast metabolizes glucose. To understand the differences between glucose and engineered xylose metabolic networks, we performed a flux balance analysis (FBA) and calculated extreme pathways using a stoichiometric model that describes the biochemistry of yeast cell growth. FBA predicted that the ethanol yield from xylose exhibits a maximum under oxygen-limited conditions, and a fermentation experiment confirmed this finding. Fermentation results were largely consistent with in silico phenotypes based on calculated extreme pathways, which displayed several phases of metabolic phenotype with respect to oxygen availability from anaerobic to aerobic conditions. However, in contrast to the model prediction, xylitol production continued even after the optimum aeration level for ethanol production was attained. These results suggest that oxygen (or some other electron accepting system) is required to resolve the redox imbalance caused by cofactor difference between xylose reductase and xylitol dehydrogenase, and that other factors limit glycolytic flux when xylose is the sole carbon source.

Cell Proliferation↗
PubMed2025

A genome-scale metabolic reconstruction resource of 247,092 diverse human microbes spanning multiple continents, age groups, and body sites.

Genome-scale modeling of microbiome metabolism enables the simulation of diet-host-microbiome-disease interactions. However, current genome-scale reconstruction resources are limited in scope by computational challenges. We developed an optimized and highly parallelized reconstruction and analysis pipeline to build a resource of 247,092 microbial genome-scale metabolic reconstructions, deemed APOLLO. APOLLO spans 19 phyla, contains >60% of uncharacterized strains, and accounts for strains from 34 countries, all age groups, and multiple body sites. Using machine learning, we predicted with high accuracy the taxonomic assignment of strains based on the computed metabolic features. We then built 14,451 metagenomic sample-specific microbiome community models to systematically interrogate their community-level metabolic capabilities. We show that sample-specific metabolic pathways accurately stratify microbiomes by body site, age, and disease state. APOLLO is freely available, enables the systematic interrogation of the metabolic capabilities of largely still uncultured and unclassified species, and provides unprecedented opportunities for systems-level modeling of personalized host-microbiome co-metabolism.

Humans↗
PubMed2026

Multimodal Analysis Reveals Aberrant Expression of SUMO2 and Its Significant Association With Key Mechanisms of Metabolic Pathways in Hepatocellular Carcinoma.

BACKGROUND: Hepatocellular carcinoma (HCC) is the third leading cause of cancer-related deaths worldwide. However, the role of small ubiquitin-like modifier 2 (SUMO2), a core member of the small ubiquitin-like modifier (SUMO) family, regarding its expression patterns and metabolism-related functions in HCC remains inadequately understood. METHODS: A multidimensional analytical framework was applied, integrating immunohistochemistry (153 HCC vs. 21 non-HCC samples), proteomics (159 paired samples), bulk transcriptomics (3240 HCC vs. 2267 non-HCC samples), single-cell RNA sequencing (RNA-seq) (10 HCC vs. 8 non-HCC samples), spatial transcriptomics, and external CRISPR/Cas9 functional genomics data. Systematic analyses included standardized mean difference (SMD), pathway enrichment, pseudotime trajectory inference, in silico knockout, cell-cell communication, metabolic flux scoring, immune infiltration, clinical correlation, drug sensitivity prediction, and molecular docking. RESULTS: At the protein level, immunohistochemistry (nuclear positivity) and external proteomic data collectively demonstrated consistent SUMO2 overexpression in HCC. Consistent upregulation was also observed at the mRNA level across large-scale cohorts. Single-cell RNA-seq and spatial transcriptomics localized SUMO2 enrichment to malignant hepatocytes and tumor-dominant regions. CRISPR-mediated SUMO2 knockout suppressed proliferation in multiple HCC cell lines. Mechanistically, high SUMO2 expression was significantly associated with metabolic reprogramming involving glycolysis/gluconeogenesis, pyruvate metabolism, and the tricarboxylic acid cycle. SUMO2-high malignant hepatocyte subpopulations exhibited enhanced activity of the macrophage migration inhibitory factor signaling axis and enhanced iron-sensor interactions. Further, the immune infiltration analysis revealed a negative correlation between SUMO2 expression and M1 macrophages and a positive correlation between follicular helper T cells and regulatory T cells. Clinically, elevated SUMO2 levels were found to be associated with adverse prognostic features. Furthermore, high SUMO2 expression was associated with increased sensitivity to dasatinib, and molecular docking simulations predicted potential binding between SUMO2 and dasatinib, with a Vina score of -8.5 kcal/mol. CONCLUSIONS: SUMO2 is aberrantly expressed at the protein, mRNA, single-cell, and spatial transcriptomic levels in HCC and is significantly associated with metabolic reprogramming and altered migration inhibitory factor (MIF)-mediated intercellular communication, suggesting its potential as a novel biomarker for diagnosis and treatment.

Humans↗
PubMed2006

Algorithmic and complexity results for decompositions of biological networks into monotone subsystems.

A useful approach to the mathematical analysis of large-scale biological networks is based upon their decompositions into monotone dynamical systems. This paper deals with two computational problems associated to finding decompositions which are optimal in an appropriate sense. In graph-theoretic language, the problems can be recast in terms of maximal sign-consistent subgraphs. The theoretical results include polynomial-time approximation algorithms as well as constant-ratio inapproximability results. One of the algorithms, which has a worst-case guarantee of 87.9% from optimality, is based on the semidefinite programming relaxation approach of Goemans-Williamson [Goemans, M., Williamson, D., 1995. Improved approximation algorithms for maximum cut and satisfiability problems using semidefinite programming. J. ACM 42 (6), 1115-1145]. The algorithm was implemented and tested on a Drosophila segmentation network and an Epidermal Growth Factor Receptor pathway model, and it was found to perform close to optimally.

Algorithms↗
PubMed2025

Engineering of xylose metabolic pathways in Rhodotorula toruloides for sustainable biomanufacturing.

The oleaginous yeast Rhodotorula toruloides is a promising microbial cell factory for the sustainable production of biofuels and value-added chemicals from renewable carbon sources. Unlike the conventional yeast Saccharomyces cerevisiae, R. toruloides can naturally metabolize xylose, the second most abundant sugar in lignocellulosic hydrolysates. However, its native xylose metabolism is inefficient, characterized by slow xylose uptake and accumulation of D-arabitol. Moreover, despite its phenotype, research on the enzymes involved in xylose metabolism has yet to reach a consensus. Therefore, this review provides a comprehensive analysis of the non-canonical xylose metabolism in R. toruloides, focusing on the properties of key enzymes involved in xylose metabolism. Native xylose reductase and xylitol dehydrogenase exhibit broad substrate promiscuity compared to their counterparts in the xylose-fermenting Scheffersomyces stipitis. Additionally, the absence of xylulokinase expression under xylose-utilizing conditions redirects metabolism toward D-arabitol accumulation. Consequently, D-arabitol dehydrogenases and ribulokinase play essential roles in the xylose metabolism of R. toruloides. These findings highlight the fundamental differences between R. toruloides xylose metabolism and the oxidoreductase pathways observed in other xylose-fermenting yeast, providing insights for metabolic engineering strategies to improve xylose utilization and enhance bioconversion of cellulosic hydrolysates to different bioproducts by R. toruloides.

Xylose↗
PubMed2006

Design, construction and performance of the most efficient biomass producing E. coli bacterium.

Inverse metabolic engineering based on elementary mode analysis was applied to maximize the biomass yield of Escherchia coli MG1655. Elementary mode analysis was previously employed to identify among 1691 possible pathways for cell growth the most efficient pathway with maximum biomass yield. The metabolic network analysis predicted that deletion of only 6 genes reduces the number of possible elementary modes to the most efficient pathway. We have constructed a strain containing these gene deletions and we evaluated its properties in batch and in chemostat growth experiments. The results show that the theoretical predictions are closely matched by the properties of the designed strain.

Biomass↗
PubMed2025

Versatile sugar and valerate metabolic pathways in Paraburkholderia xenovorans LB400 enable tailored poly(3-hydroxybutyrate-co-3-hydroxyvalerate) production.

Poly(3-hydroxybutyrate) and poly(3-hydroxybutyrate-co-3-hydroxyvalerate) polymers are accumulated by diverse prokaryotes. Their distinct monomer compositions enable their use as tailored bioplastics. The aims were to characterize the poly(3-hydroxybutyrate) and poly(3-hydroxybutyrate-co-3-hydroxyvalerate) synthesis by Paraburkholderia xenovorans LB400 using different sugars and valerate, and to gain genome-oriented insights into polyhydroxyalkanoate production. d-Glucose, d-mannitol, d-gluconate, and d-xylose were evaluated as sole carbon sources or supplemented with valerate. Polyhydroxyalkanoates synthesized by strain LB400 were characterized through GC-MS, GC-FID, FTIR, and 1H and 13C-NMR. P. xenovorans LB400 reached 1.00-1.39 g L-1 of dry cell weight (DCW) with a P(3HB) content of 21-43% w w-1 when grown on different sugars. The addition of valerate to the sugar-grown LB400 cultures yielded a DCW of 1.79 to 2.29 g L-1 and a P(3HB-co-3HV) content of 50.0‒51.2% w w-1, with varying 3HV compositions (28‒43 mol%). The highest 3HV incorporation was observed with d-xylose and valerate. Genomic analyses of strain LB400 revealed key elements of sugar metabolism influencing growth, polymer accumulation, and monomer composition. LB400 genome encodes the PhaJ-like R-specific hydratase and FadJ epimerase, which are potentially useful for modulating copolymer composition. PHA production under bioreactor conditions was evaluated. In a bioreactor fed with d-glucose, LB400 achieved a P(3HB) concentration of 2.2 g L-1. These findings highlight the metabolic versatility of P. xenovorans LB400 in utilizing diverse sugars to produce either P(3HB) or tailor-made P(3HB-co-3HV), supporting the development of bioplastics for specific applications. KEY POINTS: • Strain LB400 produced P(3HB-co-3HV) from various sugars and valerate. • Sugar type drives LB400 PHA copolymer synthesis and composition. • Strain LB400 PHA production was scaled up to a bioreactor.

Polyesters↗