Using mass-spectrometry based tools to optimize production of molecules of industrial interest in the microorganism Pseudomonas putida.Nathalie Munoz 1, Young-Mo Kim 1, Swarnendu Tripathi 2, Christopher Johnson 3, Davinia Salvachua 3, Sandra Notonier 3, Peter St John 3, Jamie Meadows 4, Jeremy Zucker 1, Kristin Burnum-Johnson 1, Carrie Nicora 1, Mark Butcher 1, John Gladden 4, Gregg Beckham 3, Jon Magnuson 2. 1 Earth and Biological Sciences Directorate, PNNL, Richland WA. 2. Energy and Environment Directorate, PNNL, Richland, WA 3

National Bioenergy Center, NREL, Golden CO. 4 Joint BioEnergy Institute, Biological Systems & Engineering Division, Lawrence Berkeley National Laboratory, Emeryville CA

Introduction

Overview Methods Results

AcknowledgementsThis work was part of the DOE Agile BioFoundry (http://agilebiofoundry.org) supported by the U.S. Department of Energy, Energy Efficiency and Renewable Energy, Bioenergy Technologies Office. This Samples were analyzed using capabilities developed under the support of NIH National Institute of General Medical Sciences (8 P41 GM103493-10). This project was supported by the U.S. Department of Energy (DOE) Office of Biological and Environmental Research (OBER) Pan-omics program at Pacific Northwest National Laboratory (PNNL) and performed in the Environmental Molecular Sciences Laboratory, a DOE OBER national scientific user facility on the PNNL campus. PNNL is a multiprogram national laboratory operated by Battelle for the DOE under contract DE-AC05-76RL01830.

References1. Enhancing muconic acid production from glucose and lignin-derived aromatic compounds via increased

protocatechuate decarboxylase acitivity. Johnson, C.W. et al. Metabolic engineering communications (2016) 3. 111—119.

2. The JBEI quantitative metabolic modeling library (jQMM) : a python library for modeling microbial metabolism. Birkel, G. et al. BMC informatics (2017) 18: 205.

3. Pseudomonas putida KT2440 strain metabolizes glucose through a cycle formed by enzymes of the Entner-Doudoroff, Embden-Meyerhoff-Parnas, and pentose phosphate pathways. Nickel, P.I. et al. The journal of biological chemistry (2015). 290: 43, 25920-25932.

ConclusionsPrincipal component analysis of intracellular metabolites

CONTACT: Nathalie Munoz, Ph.D.Biological Sciences DivisionPacific Northwest National LaboratoryE-mail: nathalie.munozmunoz@pnnl.gov

• Pseudomonas putida was genetically modified to enhance the production of muconic acid, a precursor to the commodity chemical adipic acid.

• Metabolomic studies, based on a GC-MS platform, were used to compare the metabolism of two muconic acid producing strains.

• To gain a better insight into the metabolism of these strains, 13C-labeled glucose was used as tracer and 13C-MFA was performed.

• Our aim is to identify potential bottlenecks and provide metabolic engineering strategies for higher production of the target molecule.

• High amounts of gluconic and 2-ketogluconic acid in the supernatant of P.putida CJ442, combined with the intracellular 13C-labeling results, indicate that gluconic is strictly derived from glucose and a significant leakage of carbon flux goes towards its production.

• This is in line with our findings in a P. putida strain modified to produce 3-oxoadipic acid, in which also large amounts of gluconicand 2-ketogluconic were measured (0.63 and 4.60 g/L)..

• No gluconic acid nor 2-ketogluconic acid is observed in the P. putida strain CJ522, confirming the knocking out of gcd.

• 13C-MFA indicates that pentose phosphate and Entner-Doudoroffpathways are more active in P. putida CJ522.

• Knocking out of gcd improved production of muconic acid, however, disagreement in fitting (especially in CJ522) suggest there are possibly fluxes that are not being taken into account and leakage could be occurring there.

• We plan to integrate the fluxes found from the 13C-labeling experiments to the proteomics results to further validate the predicted fluxes.• The Agile Biofoundry (ABF) is a consortium of DOE

National Labs and was established to accelerate the development of bacterial or fungal strains capable of producing biochemicals or biofuels.

• PNNL provides proteomics and metabolomics analytical capabilities on the generated samples.

• In the pilot phase of ABF, P. putida KT2440 was engineered to produce 3-oxoadipic acid. Low concentrations of the acid were measured while large amounts of gluconic and 2-ketogluconic acid were detected in medium.

• In this particular project, P. putida KT2440 wasmodified to produce muconic acid, which can be converted to adipic acid. The latter is a precursor to nylon 6,6 and other polymers with a market volume of 2.6 million tons/year (1).

• With the mass distribution vectors (MDV) measured for metabolites in key pathways in the 13C-labeled experiment and quantitation of extracellular metabolites (2,3), the metabolic model is constrained to perform 13C-MFA and metabolic fluxes in the P.putida CJ442 and CJ522 are established.

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Glucose and organic acids measured in supernatant of the strains in cells at OD 0.7.

Extra- and intra-cellular metabolites of P.putidaCJ442 and CJ522 in different growth phases

midlog stat death midlog stat death0.0E+00

1.0E+08

2.0E+08

3.0E+08

4.0E+08

5.0E+08

6.0E+08

Peak

are

a

Growth phase

CJ442 CJ522

midlog stat death midlog stat death0.0E+00

1.0E+07

2.0E+07

3.0E+07

4.0E+07

5.0E+07

6.0E+07

Peak

are

aGrowth phase

CJ442 CJ522

midlog stat death midlog stat death0.0E+00

1.0E+07

2.0E+07

3.0E+07

4.0E+07

5.0E+07

Peak

are

a

Growth area

CJ442 CJ522

midlog stat death midlog stat death0.0E+00

1.0E+07

2.0E+07

3.0E+07

4.0E+07

5.0E+07

6.0E+07

7.0E+07

Peak

are

a

Growth phase

CJ442 CJ522

Intracellular Extracellular

c,c-muconic acid

gluconic acid

2-ketogluconic acid

midlog stat death midlog stat death0.0E+00

2.0E+08

4.0E+08

6.0E+08

8.0E+08

1.0E+09

Peak

are

a

Growth phase

CJ442 CJ522

midlog stat death midlog stat death0.0E+00

5.0E+06

1.0E+07

1.5E+07

2.0E+07

2.5E+07

3.0E+07

3.5E+07

4.0E+07

4.5E+07

Peak

are

a

Growth phase

CJ442 CJ522

• The strain CJ442 was generated from the P. putida strain KT2440 which had been modified to express genes that increase the production of muconic acid and decrease its conversion to other metabolites.

• P. putida CJ522 was engineered from CJ442 and both share multiple gene modifications, however the main difference is the knocking out of glucose dehydrogenase (gcd) in CJ522.

• For the initial round of experiments, strains were grown in a bioreactor and samples were collected at different growth phases. Global metabolo-mics in an Agilent single quad GC-MS instrument was acquired.

• After this initial experiment, P.putida strains were cultured in medium which contained a mixture of 1-13C labeled and uniformly labeled glucose 4:1 at a final concentration of 5 g/L.

• Peak areas of the unlabeled metabolites and their corresponding isotopomers were obtained and corrected taking into account natural abundance of isotopes.

• Metabolites in spent supernatant were quantified and with this and MDV’s, the computational model was optimized and fluxes for different reactions in these strains calculated.

Main alterations in CJ522 metabolic pathways

Concentration (g/L) CJ442 CJ522glucose 1.94 4.24cis, cis-muconic acid 0.16 0.532-ketogluconic acid 2.68 0.02gluconic acid 0.57 0.01

Experimental MDV (red) vs calculated MDV’s implied from predicted fluxes (blue).

Comparison in fluxes of CJ442 vs CJ522.

• Mass isotopomer distribution has been measured for 19 metabolites in the P. putida modified strains.

• The aim in 13C MFA is to reduce the objective function that compares experimental vs predicted MDV’s. For CJ442 OF = 13.7, for CJ522 OF= 31.8.

• Label incorporation is detected in metabolites of EMP, PP pathway, peripheral pathway and muconic acid pathway (target) among others.

General workflow in metabolomics experiments

Samples extracted with MPlex(chloroform:methanol:water).

Dried extracts were derivatized with MSTFA to make them suitable for GC. Inject derivatized

samples in GC-Q MS and GC-QExactive.

O

O

O SiSi

RT: 10.85 - 24.92 SM: 7G

11 12 13 14 15 16 17 18 19 20 21 22 23 24Time (min)

0

5

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15

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Rel

ativ

e A

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23.27

15.80

21.7218.81

21.2018.44

15.9813.25

22.5817.6413.82 19.24 21.1220.07

23.3518.2416.46 20.22

11.64 12.87 14.33 23.1317.23 20.9421.90 23.76 24.5816.6914.69 19.3011.17 12.29 15.41 20.5219.68 22.31

NL:4.46E9TIC MS cj442_lab_r2

Analyze data acquired using targeted screening method on TraceFinder(Thermo).