In silico-guided engineering of Pseudomonas putida towards growth under micro-oxic conditions

Linde F.C. Kampers, Ruben G.A. van Heck, Stefano Donati, Edoardo Saccenti, Rita J.M. Volkers, Peter J. Schaap, Maria Suarez-Diez, Pablo I. Nikel, Vitor A.P. Martins Dos Santos*

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

BACKGROUND: Pseudomonas putida is a metabolically versatile, genetically accessible, and stress-robust species with outstanding potential to be used as a workhorse for industrial applications. While industry recognises the importance of robustness under micro-oxic conditions for a stable production process, the obligate aerobic nature of P. putida, attributed to its inability to produce sufficient ATP and maintain its redox balance without molecular oxygen, severely limits its use for biotechnology applications. RESULTS: Here, a combination of genome-scale metabolic modelling and comparative genomics is used to pinpoint essential [Formula: see text]-dependent processes. These explain the inability of the strain to grow under anoxic conditions: a deficient ATP generation and an inability to synthesize essential metabolites. Based on this, several P. putida recombinant strains were constructed harbouring acetate kinase from Escherichia coli for ATP production, and a class I dihydroorotate dehydrogenase and a class III anaerobic ribonucleotide triphosphate reductase from Lactobacillus lactis for the synthesis of essential metabolites. Initial computational designs were fine-tuned by means of adaptive laboratory evolution. CONCLUSIONS: We demonstrated the value of combining in silico approaches, experimental validation and adaptive laboratory evolution for microbial design by making the strictly aerobic Pseudomonas putida able to grow under micro-oxic conditions.

Original languageEnglish
Article number179
JournalMicrobial Cell Factories
Volume18
Issue number1
DOIs
Publication statusPublished - 22 Oct 2019

Keywords

  • Anaerobiosis
  • Comparative genomics
  • Constraint-based metabolic modelling
  • Domainome analysis
  • Microbial physiology
  • Synthetic biology

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