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Title: High-quality genome-scale metabolic modelling of Pseudomonas putida highlights its broad metabolic capabilities

Journal Article · · Environmental Microbiology
ORCiD logo [1];  [2];  [3];  [4];  [5];  [6];  [7]; ORCiD logo [8];  [9];  [7]
  1. Centro Nacional de Biotecnología (CNB‐CSIC) Madrid (Spain). Dept. of Systems Biology; Univ. of California, San Diego, La Jolla, CA (United States). Dept. of Bioengineering
  2. Univ. of California, San Diego, La Jolla, CA (United States). Dept. of Bioengineering Univ. of Nebraska, Lincoln, NE (United States). Dept. of Chemical and Biomolecular Engineering
  3. Univ. of Iceland, Reykjavík (Iceland). Center for Systems Biology
  4. Centro Nacional de Biotecnología (CNB‐CSIC) Madrid (Spain). Dept. of Systems Biology
  5. Estación Experimental del Zaidín (CSIC) Granada (Spain). Dept. of Environmental Protection
  6. Univ. of California, San Diego, La Jolla, CA (United States). Dept. of Bioengineering
  7. Univ. of California, San Diego, La Jolla, CA (United States). Dept. of Bioengineering
  8. Estación Experimental del Zaidín (CSIC) Granada (Spain). Dept. of Environmental Protection
  9. Univ. of Nebraska, Lincoln, NE (United States). Dept. of Chemical and Biomolecular Engineering

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.

Research Organization:
Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
Sponsoring Organization:
USDOE Office of Science (SC)
Grant/Contract Number:
AC02-05CH11231; DE‐AC02‐05CH11231
OSTI ID:
1574030
Alternate ID(s):
OSTI ID: 1574031; OSTI ID: 1625852
Journal Information:
Environmental Microbiology, Vol. 22, Issue 1; ISSN 1462-2912
Publisher:
WileyCopyright Statement
Country of Publication:
United States
Language:
English
Citation Metrics:
Cited by: 80 works
Citation information provided by
Web of Science

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iML1515, a knowledgebase that computes Escherichia coli traits journal October 2017
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Unique attributes of cyanobacterial metabolism revealed by improved genome-scale metabolic modeling and essential gene analysis journal December 2016
Analysis of optimality in natural and perturbed metabolic networks journal November 2002
Building the repertoire of dispensable chromosome regions in Bacillus subtilis entails major refinement of cognate large-scale metabolic model journal October 2012
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Identification of conditionally essential genes for growth of Pseudomonas putida KT2440 on minimal medium through the screening of a genome-wide mutant library journal March 2010
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Genome-Scale Reconstruction and Analysis of the Pseudomonas putida KT2440 Metabolic Network Facilitates Applications in Biotechnology journal October 2008
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