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Detalle de: High‐quality genome‐scale metabolic modelling of Pseudomonas putida highlights its broad metabolic capabilities

Title High‐quality genome‐scale metabolic modelling of Pseudomonas putida highlights its broad metabolic capabilities
Abstract

Summary Genome‐scale reconstructions of metabolism are computational species‐specific knowledge bases able to compute systemic metabolic properties. We present a comprehensive and validated reconstruction of the biotechnologically relevant bacterium Pseudomonas putida KT2440 that greatly expands computable predictions of its metabolic states. The reconstruction represents a significant reactome expansion over available reconstructed bacterial metabolic networks. Specifically, i JN1462 (i) incorporates several hundred additional genes and associated reactions resulting in new predictive capabilities, including new nutrients supporting growth; (ii) was validated by in vivo growth screens that included previously untested carbon (48) and nitrogen (41) sources; (iii) yielded gene essentiality predictions showing large accuracy when compared with a knock‐out library and Bar‐seq data; and (iv) allowed mapping of its network to 82 P . putida sequenced strains revealing functional core that reflect the large metabolic versatility of this species, including aromatic compounds derived from lignin. Thus, this study provides a thoroughly updated metabolic reconstruction and new computable phenotypes for P . putida , which can be leveraged as a first step toward understanding the pan metabolic capabilities of Pseudomonas .

Apa Reference Nogales, J., Mueller, J., Gudmundsson, S., Canalejo, F.J., Duque, E., Monk, J., Feist, A.M., Ramos, J.L., Niu, W. & Palsson, B.O. (2019). High‐quality genome‐scale metabolic modelling of Pseudomonas putida highlights its broad metabolic capabilities. Environmental Microbiology, 22(1), 255-269. 10.1111/1462-2920.14843
Doi 10.1111/1462-2920.14843
Publication Date 11 Nov 2019
Publication Type Article
Researcher Juan Nogales
Ontology Metabolic modelling, Mapping, Pathway analysis
Research Groups Systems Biotechnology Group
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