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The revisited genome of Pseudomonas putida KT2440 enlightens its value as a robust metabolic chassis

Belda, Eugeni, van Heck, Ruben G. A., José Lopez‐Sanchez, Maria, Cruveiller, Stéphane, Barbe, Valérie, Fraser, Claire, Klenk, Hans‐Peter, Petersen, Jörn, Morgat, Anne, Nikel, Pablo I., Vallenet, David, Rouy, Zoé, Sekowska, Agnieszka, Martins dos Santos, Vitor A. P., de Lorenzo, Víctor, Danchin, Antoine, Médigue, Claudine·2016·Environmental Microbiology
DOI 10.1111/1462-2920.13230
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Abstract

Summary: By the time the complete genome sequence of the soil bacterium Pseudomonas putida KT2440 was published in 2002 (Nelson et al ., ) this bacterium was considered a potential agent for environmental bioremediation of industrial waste and a good colonizer of the rhizosphere. However, neither the annotation tools available at that time nor the scarcely available omics data—let alone metabolic modeling and other nowadays common systems biology approaches—allowed them to anticipate the astonishing capacities that are encoded in the genetic complement of this unique microorganism. In this work we have adopted a suite of state‐of‐the‐art genomic analysis tools to revisit the functional and metabolic information encoded in the chromosomal sequence of strain KT2440. We identified 242 new protein‐coding genes and re‐annotated the functions of 1548 genes, which are linked to almost 4900 PubMed references. Catabolic pathways for 92 compounds (carbon, nitrogen and phosphorus sources) that could not be accommodated by the previously constructed metabolic models were also predicted. The resulting examination not only accounts for some of the known stress tolerance traits known in P. putida but also recognizes the capacity of this bacterium to perform difficult redox reactions, thereby multiplying its value as a platform microorganism for industrial biotechnology.

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Fig. 1 Schematic representation of the Glycine-Betaine, Carnitine and Choline metabolism in P. putida KT2440. Blue arrows represent transport reactions ( dotted lines ) and cytoplasmic reactions ( continue lines ) that were present in previous KT2440 metabolic model reconstructions (Bigg 5 i JN746, TOBIN 5 i JP968; different naming spaces). Green arrows show new GPR associations curated during our reannotation process (they were missing in previous KT2440 metabolic reconstructions) CoA, coenzyme A.
Fig. 1 Schematic representation of the Glycine-Betaine, Carnitine and Choline metabolism in P. putida KT2440. Blue arrows represent transport reactions ( dotted lines ) and cytoplasmic reactions ( continue lines ) that were present in previous KT2440 metabolic model reconstructions (Bigg 5 i JN746, TOBIN 5 i JP968; different naming spaces). Green arrows show new GPR associations curated during our reannotation process (they were missing in previous KT2440 metabolic reconstructions) CoA, coenzyme A.CC-BY-NC · From the PDF
Fig. 2 Trehalose metabolism in P. putida KT2440. ( A ) Metabolic pathway of trehalose biosynthesis in P. putida KT2440 and E. coli K12. Reactions specific to P. putida are shown in green and those specific to E. coli are shown in red . Shared reactions are represented in blue ( B ) Lineplot showing tblastX similarities between genomic regions containing genes involved in the trehalose metabolism in P. putida KT2440 and in E. coli K12 ( C ) same as (B) between P. putida KT2440 and Mycobacterium tuberculosis H37Rv. The gene cluster is splitted in three different genomic regions in M. tuberculosis H37Rv. In this organism, treS and malK genes are not fused. ADP , adenosine diphosphate; UDP , uridine diphosphate.
Fig. 2 Trehalose metabolism in P. putida KT2440. ( A ) Metabolic pathway of trehalose biosynthesis in P. putida KT2440 and E. coli K12. Reactions specific to P. putida are shown in green and those specific to E. coli are shown in red . Shared reactions are represented in blue ( B ) Lineplot showing tblastX similarities between genomic regions containing genes involved in the trehalose metabolism in P. putida KT2440 and in E. coli K12 ( C ) same as (B) between P. putida KT2440 and Mycobacterium tuberculosis H37Rv. The gene cluster is splitted in three different genomic regions in M. tuberculosis H37Rv. In this organism, treS and malK genes are not fused. ADP , adenosine diphosphate; UDP , uridine diphosphate.CC-BY-NC · From the PDF
Fig. 3 Schematic representation of the catabolism of aromatic compounds to central metabolites in P. putida KT2440 (adapted from Nogales et al ., 2008). Red compounds correspond to dead-end metabolites in i JP968 model (isolated compounds that are either only consumed or only produced by the model). Gray compounds represent aromatic compounds absents in i JP968 metabolic model. Green arrows and green genes represent new GPR associations curated during the P. putida KT2440 genome re-annotation process (they were absents in the original i JP968 model). Flux Balance Analysis (FBA) simulations over the extended i JP968 metabolic model gave rise to a functional phenotype in terms of biomass production with all these aromatic compounds as external carbon sources. OM, outer membrane; IM, inner membrane; CoA, coenzyme A; and AcCoA, acetyl-coenzyme A.
Fig. 3 Schematic representation of the catabolism of aromatic compounds to central metabolites in P. putida KT2440 (adapted from Nogales et al ., 2008). Red compounds correspond to dead-end metabolites in i JP968 model (isolated compounds that are either only consumed or only produced by the model). Gray compounds represent aromatic compounds absents in i JP968 metabolic model. Green arrows and green genes represent new GPR associations curated during the P. putida KT2440 genome re-annotation process (they were absents in the original i JP968 model). Flux Balance Analysis (FBA) simulations over the extended i JP968 metabolic model gave rise to a functional phenotype in terms of biomass production with all these aromatic compounds as external carbon sources. OM, outer membrane; IM, inner membrane; CoA, coenzyme A; and AcCoA, acetyl-coenzyme A.CC-BY-NC · From the PDF

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Metadata

Authors
Belda, E., van Heck, R. G. A., José Lopez‐Sanchez, M., Cruveiller, S., Barbe, V., Fraser, C., Klenk, H., Petersen, J., Morgat, A., Nikel, P. I., Vallenet, D., Rouy, Z., Sekowska, A., Martins dos Santos, V. A. P., de Lorenzo, V., Danchin, A., & Médigue, C.
Affiliations
  • Alternative Energies and Atomic Energy Commission (CEA), Genomic Institute & CNRS‐UMR8030 & Evry University, Laboratory of Bioinformatics Analysis in Genomics and Metabolism 2 rue Gaston Crémieux 91057 Evry France
  • Laboratory of Systems and Synthetic Biology, Wageningen University Dreijenplein 10, Building number 316 6703 HB Wageningen The Netherlands
  • Alternative Energies and Atomic Energy Commission (CEA), Genomic Institute, National Sequencing Center 2 rue Gaston Crémieux 91057 Evry France
  • Institute for Genome Sciences, Department of Microbiology and Immunology, University of Maryland School of Medicine Baltimore MD USA
  • Leibniz Institute DSMZ – German Collection of Microorganisms and Cell Cultures Braunschweig Germany
  • Swiss‐Prot Group, SIB Swiss Institute of Bioinformatics Geneva CH‐1206 Switzerland
  • Systems and Synthetic Biology Program, Centro Nacional de Biotecnología (CNB‐CSIC) C/Darwin 3 28049 Madrid Spain
  • AMAbiotics SAS, Institut du Cerveau et de la Moëlle Épinière, Hôpital de la Pitié‐Salpêtrière Paris France
Venue
Environmental Microbiology
Publisher
Wiley
Published
2016-04-28
Volume
18
Issue
10
Pages
3403-3424
ISSN
  • 1462-2912
  • 1462-2920
DOI
10.1111/1462-2920.13230
Cited by
338
References
4
Language
en
Licence
creativecommons.org/licenses/by-nc/4.0/ (vor)

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