TY - JOUR
T1 - High-quality genome-scale metabolic modelling of Pseudomonas putida highlights its broad metabolic capabilities
AU - Nogales, Juan
AU - Mueller, Joshua
AU - Gudmundsson, Steinn
AU - Canalejo, Francisco J.
AU - Duque, Estrella
AU - Monk, Jonathan
AU - Feist, Adam M.
AU - Ramos, Juan Luis
AU - Niu, Wei
AU - Palsson, Bernhard O.
N1 - Funding Information:
This work was supported by i) the European Union's Horizon 2020 Research and Innovation Programme under Grant Agreements no 635536, 686585 and 814650, ii) the Spanish Ministry of Economy and Competitiveness through funding provided to projects RobDcode (BIO2014-59528-JIN) and MEPRIVA (RTI-2018-094370-B-I00), iii) the National Science Foundation Graduate Research Fellowship Program under Grant No. 1610400, iv) Novo Nordisk Fonden contract NNF10CC1016517. This work was part of the DOE Joint BioEnergyInstitute (http://www.jbei.org) supported by the U. S. Department of Energy, Office of Science, through contract DE-AC02-05CH11231 between Lawrence Berkeley National Laboratory and the U. S. Department of Energy. The United States Government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for United States Government purposes. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of any of the agencies that funded their research. The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication. The authors thank Marc Abrams and Clive A. Dovefor critical reading of the manuscript and R. van Heck for providing the iEB1050 model.
Funding Information:
This work was supported by i) the European Union's Horizon 2020 Research and Innovation Programme under Grant Agreements no 635536, 686585 and 814650, ii) the Spanish Ministry of Economy and Competitiveness through funding provided to projects RobDcode (BIO2014‐59528‐JIN) and MEPRIVA (RTI‐2018‐094370‐B‐I00), iii) the National Science Foundation Graduate Research Fellowship Program under Grant No. 1610400, iv) Novo Nordisk Fonden contract NNF10CC1016517. This work was part of the DOE Joint BioEnergyInstitute ( http://www.jbei.org ) supported by the U. S. Department of Energy, Office of Science, through contract DE‐AC02‐05CH11231 between Lawrence Berkeley National Laboratory and the U. S. Department of Energy. The United States Government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a nonexclusive, paid‐up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for United States Government purposes. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of any of the agencies that funded their research. The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication. The authors thank Marc Abrams and Clive A. Dovefor critical reading of the manuscript and R. van Heck for providing the iEB1050 model.
PY - 2020/1/1
Y1 - 2020/1/1
N2 - 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, iJN1462 (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.
AB - 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, iJN1462 (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.
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U2 - 10.1111/1462-2920.14843
DO - 10.1111/1462-2920.14843
M3 - Article
C2 - 31657101
AN - SCOPUS:85075160411
VL - 22
SP - 255
EP - 269
JO - Environmental Microbiology
JF - Environmental Microbiology
SN - 1462-2912
IS - 1
ER -