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Secondary metabolites have crucial medicinal and industrial applications, but their alignment with primary metabolism remains unclear. As secondary metabolism depends on primary metabolism for precursor supply, we present a pan-reactome analysis of 242 Streptomyces strains to investigate their association and disconnection. This analysis includes phylogenetic grouping of the strains using genome data, and uniform manifold approximation and projection (UMAP) analysis of their genome-scale metabolic models (GEMs) and biosynthetic gene cluster (BGC) data, which represent biochemical reactions in primary and secondary metabolism. Subsequent correlation analysis of the preprocessed GEM and BGC data showed a Pearson correlation coefficient of 0.54, revealing both metabolic association and disconnection. In particular, among 47 precursors of polyketides, nonribosomal peptides, and hybrids, nine precursors required by these BGCs were predicted to be non-producible due to missing genes in primary metabolism or BGCs. The pan-reactome analysis facilitates the identification of precursor availability and metabolic gaps, providing insights into secondary metabolite biosynthesis.
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http://dx.doi.org/10.1016/j.ymben.2025.08.005 | DOI Listing |
Metab Eng
November 2025
Systems Biology and Medicine Laboratory, Department of Chemical and Biomolecular Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Republic of Korea; Systems Metabolic Engineering and Systems Healthcare Cross-Generation Collaborative Laboratory, KAIST, Daejeon,
Secondary metabolites have crucial medicinal and industrial applications, but their alignment with primary metabolism remains unclear. As secondary metabolism depends on primary metabolism for precursor supply, we present a pan-reactome analysis of 242 Streptomyces strains to investigate their association and disconnection. This analysis includes phylogenetic grouping of the strains using genome data, and uniform manifold approximation and projection (UMAP) analysis of their genome-scale metabolic models (GEMs) and biosynthetic gene cluster (BGC) data, which represent biochemical reactions in primary and secondary metabolism.
View Article and Find Full Text PDFBiomolecules
November 2022
Department of Biology and Biological Engineering, Chalmers University of Technology, Kemivägen 10, SE412 96 Gothenburg, Sweden.
Yeasts are increasingly employed in synthetic biology as chassis strains, including conventional and non-conventional species. It is still unclear how genomic evolution determines metabolic diversity among various yeast species and strains. In this study, we constructed draft GEMs for 332 yeast species using two alternative procedures from the toolbox RAVEN v 2.
View Article and Find Full Text PDFGenome Biol
June 2019
Department of Chemical and Biomolecular Engineering (BK21 Plus Program), Metabolic and Biomolecular Engineering National Research Laboratory, Institute for the BioCentury, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Republic of Korea.
Genome-scale metabolic models (GEMs) computationally describe gene-protein-reaction associations for entire metabolic genes in an organism, and can be simulated to predict metabolic fluxes for various systems-level metabolic studies. Since the first GEM for Haemophilus influenzae was reported in 1999, advances have been made to develop and simulate GEMs for an increasing number of organisms across bacteria, archaea, and eukarya. Here, we review current reconstructed GEMs and discuss their applications, including strain development for chemicals and materials production, drug targeting in pathogens, prediction of enzyme functions, pan-reactome analysis, modeling interactions among multiple cells or organisms, and understanding human diseases.
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