期刊
BIOCHEMICAL ENGINEERING JOURNAL
卷 196, 期 -, 页码 -出版社
ELSEVIER
DOI: 10.1016/j.bej.2023.108947
关键词
Metabolic network; Genome-scale model reconstruction; Gut microbiota; Prevotella copri
In this study, a genome-scale metabolic network model, iPc610, was reconstructed for P. copri DSM 18205 through an intensive manual curation process. The model consists of 1737 metabolic reactions controlled by 610 genes. The prediction performance of iPc610 was validated with experimental data, showing reliable predictions of by-product secretions under anaerobic conditions. Gene essentiality simulations identified 70 essential genes, which can be used as candidate targets in future studies to restore dysregulated gut microbiota composition in various diseases.
Prevotella copri DSM 18205 is a bacterial strain commonly encountered in human gut microbiota. Importantly, increasing evidence suggests that alterations in the abundance of P. copri are associated with numerous pathologies. A representation of metabolic interactions within bacteria is crucial to elucidate the molecular mechanisms of these disorders and, consequently, develop novel treatments. However, P. copri lacks a curated high-quality genome-scale metabolic network model. Therefore, we reconstructed a genome-scale metabolic network model for P. copri DSM 18205 in this study through an intensive manual curation process. The reconstructed model, called iPc610, consists of 1737 metabolic reactions controlled by 610 genes. The prediction performance of iPc610 was validated with experimental data from the literature, where the model reliably predicted secretions of by-products succinate and acetate using a D-glucose minimal medium under anaerobic conditions. Gene essentiality simulations identified 70 genes as essential, which can be used as candidate targets in future studies to restore dysregulated gut microbiota composition in several diseases. As a result, the reconstructed genome-scale metabolic model, iPc610, is functional and can demonstrate the metabolic behavior of the target organism with accurate predictions.
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