Engineering glucose metabolism for enhanced muconic acid production in Pseudomonas putida KT2440

Gayle J. Bentley, Niju Narayanan, Ramesh K. Jha, Davinia Salvachúa, Joshua R. Elmore, George L. Peabody, Brenna A. Black, Kelsey Ramirez, Annette De Capite, William E. Michener, Allison Z. Werner, Dawn M. Klingeman, Heidi S. Schindel, Robert Nelson, Lindsey Foust, Adam M. Guss, Taraka Dale, Christopher W. Johnson, Gregg T. Beckham

Research output: Contribution to journalArticlepeer-review

79 Scopus citations

Abstract

Pseudomonas putida KT2440 has received increasing attention as an important biocatalyst for the conversion of diverse carbon sources to multiple products, including the olefinic diacid, cis,cis-muconic acid (muconate). P. putida has been previously engineered to produce muconate from glucose; however, periplasmic oxidation of glucose causes substantial 2-ketogluconate accumulation, reducing product yield and selectivity. Deletion of the glucose dehydrogenase gene (gcd) prevents 2-ketogluconate accumulation, but dramatically slows growth and muconate production. In this work, we employed adaptive laboratory evolution to improve muconate production in strains incapable of producing 2-ketogluconate. Growth-based selection improved growth, but reduced muconate titer. A new muconate-responsive biosensor was therefore developed to enable muconate-based screening using fluorescence activated cell sorting. Sorted clones demonstrated both improved growth and muconate production. Mutations identified by whole genome resequencing of these isolates indicated that glucose metabolism may be dysregulated in strains lacking gcd. Using this information, we used targeted engineering to recapitulate improvements achieved by evolution. Deletion of the transcriptional repressor gene hexR improved strain growth and increased the muconate production rate, and the impact of this deletion was investigated using transcriptomics. The genes gntZ and gacS were also disrupted in several evolved clones, and deletion of these genes further improved strain growth and muconate production. Together, these targets provide a suite of modifications that improve glucose conversion to muconate by P. putida in the context of gcd deletion. Prior to this work, our engineered strain lacking gcd generated 7.0 g/L muconate at a productivity of 0.07 g/L/h and a 38% yield (mol/mol) in a fed-batch bioreactor. Here, the resulting strain with the deletion of hexR, gntZ, and gacS achieved 22.0 g/L at 0.21 g/L/h and a 35.6% yield (mol/mol) from glucose in similar conditions. These strategies enabled enhanced muconic acid production and may also improve production of other target molecules from glucose in P. putida.

Original languageEnglish
Pages (from-to)64-75
Number of pages12
JournalMetabolic Engineering
Volume59
DOIs
StatePublished - May 2020

Funding

This work was authored in part by Alliance for Sustainable Energy, LLC, the manager and operator of the National Renewable Energy Laboratory for the U.S. Department of Energy (DOE) under Contract No. DE-AC36-08GO28308 . This work has also been authored in part by employees of Triad National Security, LLC, operator of the Los Alamos National Laboratory under Contract No.8 9233218CNA000001 with the U.S. Department of Energy. This work was also partially authored Oak Ridge National Laboratory, which is managed by UT-Battelle, LLC, for the U.S. DOE under contract DE-AC05-00OR22725 . Funding was provided by the U.S. Department of Energy Office of Energy Efficiency and Renewable Energy Bioenergy Technologies Office (BETO) for the Agile BioFoundry (to TD (Contract NL0032182 ), AMG, and GTB). We thank Jay Fitzgerald at DOE and members of the Agile BioFoundry for helpful discussions. We thank Rita Clare for designing and preparing Figs. 2 and 4 A. We thank Babetta Marrone and Laboratory Directed Research and Development grant from Los Alamos National Laboratory (project 20160656ER ) for supporting preliminary work on sensor development. We thank Jay Huenemann for plasmid construction assistance. Joshua Vermaas wrote the Python script to calculate the specific growth rate.

Keywords

  • 2-Ketogluconate
  • Adaptive laboratory evolution
  • Biosensor
  • CatM
  • FACS
  • GacS
  • Gluconate
  • Glycolysis regulation
  • HexR
  • High throughput selection
  • Pseudomonas putida KT2440
  • cis,cis-muconic acid

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