Team:NOVA LxPortugal/Model

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In the synthetic biology field, metabolic modulation is a powerful tool for the study of biological networks, providing a non-intuitive insight into the biological system. In particular, genome-scale metabolic models are used to computationally describe stoichiometry and mass-balanced metabolic reactions in an organism using gene-protein-reaction associations1. Further simulation and optimization of these models are crucial to the development of more efficient microbial cell factories with applications in the pharmaceutic, biofuel and food industries2,3.

In this project, we aim at developing an efficient spectinabilin producing Pseudomonas putida strain. To do so, the spectinabilin production pathway was added to a P. putida genome-scale metabolic model iJN14624. The model’s stoichiometry was analysed before any further work, to guarantee its stoichiometric balance. Optflux5, a metabolic engineering software, was then used to perform optimizations using Evolutionary Algorithm (EA), a widely used optimization tool, to find genotype solutions that maximize spectinabilin production.
Therefore, the in silico analysis team had four main goals:

  • creation of a Pseudomonas putida model with the required reactions for the production of spectinabilin;
  • check the stoichiometry balance of the newly developed model (since some original reactions were unbalanced);
  • validation of the model in different environmental conditions;
  • optimization of the production of spectinabilin, while maintaining cellular viability.

Creation of the Pseudomonas putida model to produce spectinabilin

The first step was the development of a Pseudomonas putida model able to simulate the heterologous production of spectinabilin. There are already several P. putida genome-scale models so there was no need to start from scratch. The model iJN14624 was selected as the foundation for our own model since it was the most recent, complete and manually curated model for P. putida KT24404, the strain used in the wet-lab experiments. Since (S)-methylmalonyl-CoA is a precursor of spectinabilin, its production pathway was also added to the iJN1462 model, connecting with the P. putida metabolism via the Krebs cycle6,7.
The reactions added to the iJN1462 model are presented in Tables 1 and 2, with the full model available here for download.

Table 1 - Spectinabilin production reactions added to the Pseudomonas putida model.
Reaction Name Substrates Products
4-aminobenzoate N-oxygenase
  • 4-aminobenzoate
  • Reduced ferredoxin [iron-sulfur] cluster
  • 2 O2
  • 4-nitrobenzoate
  • Oxidized ferredoxin [iron-sulfur] cluster
  • 2 H2O
4-nitrobenzoate-CoA ligase
  • 4-nitrobenzoate
  • ATP
  • Coenzyme A
  • 4-nitrobenzoyl-CoA
  • AMP
  • Diphosphate
Spectinabilin polyketide synthase
  • 4-nitrobenzoyl-CoA
  • Malonyl-CoA
  • 6 (S)-methylmalonyl-CoA
  • 6 NADPH
  • 4 H+
  • Demethyldeoxyspectinabilin
  • 7 CO2
  • 8 CoA
  • 6 NADP+
  • 5 H2O
Demethyldeoxyspectinabilin O-methyltransferase
  • S-adenosyl-L-methionine
  • Demethyldeoxyspectinabilin
  • S-adenosyl-L-homocysteine
  • Deoxyspectinabilin
  • H+
Deoxyspectinabilin monooxygenase
  • Deoxyspectinabilin
  • 4 Reduced ferredoxin [iron-sulfur] cluster
  • 2 O2
  • 4 H+
  • Spectinabilin
  • 4 Oxidized ferredoxin [iron-sulfur] cluster
  • 3 H2O
Spectinabilin export
  • Spectinabilincytosol
  • Spectinabilinextracellular
Table 2 - (S)-methylmalonyl-CoA production reactions added to the Pseudomonas putida model.
Reaction Name Substrates Products
(R)-methylmalonyl-CoA CoAcarbonylmutase
  • Succinyl-CoA
  • (R)-methylmalonyl-CoA
Methylmalonyl-CoA epimerase
  • (R)-methylmalonyl-CoA
  • (S)-methylmalonyl-CoA

Verify the stoichiometric balance of the model

After building the spectinabilin-producing model, we verified the stoichiometry of its reactions with Optflux5. Optflux is an open-access metabolic engineering software developed in Portugal by our PI when she was at the Centre of Biological Engineering (Universidade do Minho) and supported by SilicoLife Lda5. This software’s main features include simulation and optimization of stoichiometric models, but it also possesses many plug-ins with interesting capabilities, such as Balance Checker, which automatically calculates disparities in stoichiometric balance. Using this plug-in, we fixed the stoichiometry of several reactions.

Validation of the Pseudomonas putida model in different media

The second step was the validation of the spectinabilin-producing model by performing wild-type simulations with pFBA9, a simulation method that allows maximizing the objective function while minimizing the total sum of the remaining fluxes. Two separate objective functions were maximized: the biomass growth rate and the production of spectinabilin. The simulations were performed with three different medium formulations, all presented in the supplementary material:

  1. Minimal medium predefined in the model;
  2. M9 minimal medium, previously used for Pseudomonas putida;
  3. Luria Bertani (LB) growth medium, also previously used for P. putida.

The results of these simulations are presented below in Table 3.

Table 3 - Values of the production of biomass and spectinabilin in different media.
Objective Function
Maximization of the production of biomass (h-1) Maximization of the production of spectinabilin mmol.(gDW.h)-1
Media Minimal medium 0.737 0.995
M9 medium 1.430 1.579
LB medium 1.710 3.449

Since the simulations using LB medium allowed to achieve the highest biomass and spectinabilin fluxes (1.710 h-1 and 3.449 mmol.(gDW.h)-1), respectively, all the optimization process was proceeded using this condition.

Optimization of spectinabilin production

Lastly, we decided to perform an under/overexpression (UO) and knockout (KO) optimization task to find an optimum genotype that provides the highest production flux of spectinabilin while maintaining a reasonable value of biomass growth rate. To do so, we used Evolutionary Algorithms (EA)8 with the simulation method LMOMA10 (Figure 1). EA searches for the optimal set of genomic conditions that maximizes fitness, which in our case is the flux for the production of the target compound (spectinabilin)7. This method is inspired by biological evolution, and uses candidate solutions to produce “offsprings” which will inherit some of the best characteristics of their “parents'', thus creating a new generation of possible solutions whose fitness will be evaluated. This continues for a set number of iterations/generations. LMOMA is a simulation method that minimizes the Manhattan distance between the flux distribution of the mutant strain and the wild-type strain10. This simulation method was chosen over the pFBA, since the latter method couldn’t find results.

simulation and optimization process
Figure 1 - Optimization process flow.

The number of genomic alterations or KOs allowed in the optimization was six, to avoid solutions with a relatively higher number of modifications, which would difficult the application of those results in an experimental setting. The objective functions used were BPCY (Biomass-product coupled yield) or Yield (Product Yield with minimum biomass). The first one balances both maximization of the cellular growth and the production of spectinabilin8, while Yield targets the maximization of the desired compound but maintains minimum biomass levels8. In our optimizations that minimum was 10% of biomass.
The results obtained were filtered based on the following criteria:

  1. spectinabilin production above 0;
  2. biomass production above 0;
  3. The results that have a minimal KO or UO of genes.

The best results obtained are presented in Table 4.

Table 4 - Best genomic conditions obtained by the optimization by Optflux software for the desired objective function.
Solution Genetic technique Gene ID UO Factor Protein/Enzyme Biomass (h-1) Spectinabilin (mmol.(gDW.h)-1)
1 KO G_PP_3124 - Short chain fatty acid transporter 0.178 1.560
G_PP_0813 - Cytochrome bo terminal oxidase subunit I
G_PP_1444 - Quinoprotein glucose dehydrogenase
G_PP_0612 - FAD-dependent glycine/D-amino acid oxidase
G_PP_4191 - Succinate dehydrogenase / fumarate reductase, flavoprotein subunit
2 UO G_PP_3570 0.03125x Carbohydrate-selective porin 0.339 1.294
G_PP_4678 0.03125x Ketol-acid reductoisomerase
G_PP_5419 0.5x F-type H+-transporting ATPase subunit a
G_PP_4192 0.03125x Succinate dehydrogenase / fumarate reductase, membrane anchor subunit
3 UO G_PP_3570 0.03125x Carbohydrate-selective porin 0.401 1.186
G_PP_1620 0.03125x 5'-nucleotidase SurE
G_PP_1665 0.5x Phosphoribosylformylglycinamidine cyclo-ligase
G_PP_5419 0.5x F-type H+-transporting ATPase subunit a
G_PP_4192 0.03125x Succinate dehydrogenase / fumarate reductase, membrane anchor subunit
4 UO G_PP_3570 0.03125x Carbohydrate-selective porin 0.384 1.136
G_PP_5419 0.5x F-type H+-transporting ATPase subunit a
G_PP_4192 0.125x Succinate dehydrogenase / fumarate reductase, membrane anchor subunit

Since our objective is to increase the production of spectinabilin, we decided to focus on solution 1, the solution with the KOs, which presents the highest spectinabilin flow. The results are present in Table 5.

Table 5 - Genes KOs obtained from the optimization with the higher production of spectinabilin and respectively protein and reactions.
Gene ID Gene KO Protein Reaction inactivated
G_PP_0612 thiO FAD-dependent glycine/D-amino acid oxidase Glycine + H2O + O2 ⇌ Glyoxylate + H2O2 + NH4+
G_PP_0813 cyoB Cytochrome bo terminal oxidase subunit I 4 H+citoplasma + 0.5 O2 + Ubiquinol-8 ⇌ H2O + Ubiquinone-8 + 4 H+periplasm
G_PP_3124 PP RS16290 Short chain fatty acid transporter Acetoacetateperiplasm + H+periplasm ⇌ Acetoacetatecitoplasma + H+citoplasma
G_PP_4191 sdhA Succinate dehydrogenase flavoprotein subunit Ubiquinone-8 + Succinate ⇌ Fumarate + Ubiquinol-8
G_PP_1444 gcd Quinoprotein glucose dehydrogenase Ubiquinone-8 + D-glucose + H2O ⇌ Ubiquinol-8 + D-gluconate + H+

To better understand the variation of the metabolism between the wild-type and the best mutant obtained in the optimizations (Solution 1), we decided to perform a flux analysis, presented in Table 6.

Table 6 - Reactions with higher flux values in the mutant strain versus the wild type strain.
Reaction Variation on the flux between the mutant and wild-type strain Reaction Variation on the flux between the mutant and wild-type strain
D-lactate transport 39.05 Adenylate kinase 1.60
Glucose-6-phosphate dehydrogenase 15.55 4-aminobenzoate N-oxygenase 1.16
6-phosphogluconolactonase 15.55 Demethyldeoxyspectinabilin O-methyltransferase 1.16
D-glucose transport via ABC 15.41 Deoxyspectinabilin monooxygenase 1.16
Hexokinase D-glucose ATP 15.41 4-Nitrobenzoate-CoA ligase 1.16
H2O transport 15.31 3-5-cyclic nucleotide phosphodiesterase 1.16
Succinyl-CoA synthetase 12.39 R spectinabilin polyketide synthase 1.16
R cytochrome oxidase bd ubiquinol 8 11.08 4-aminobenzoate synthase 1.15
Methylmalonyl-CoA-carbonylmutase 8.09 4-amino-4-deoxychorismatesynthase 1.15
D-lactate dehydrogenase 7.17 Adenosine kinase 1.15
D-lactate transport via proton symport 7.17 Adenosylhomocysteinase 1.15
L-aspartase 3.86 Methionine synthase 1.15
Phosphate transport via ABC system 3.76 5-10-methylenetetrahydrofolate reductase NADH 1.15
L-aspartate transport in via proton symport 3.49 R methionine adenosyltransferase 1.15
L-aspartate transport in via periplasm 3.49 R Glycine Cleavage System 1.14
R NADH dehydrogenase ubiquinone 8 1.20

Through the analysis of these results, we can observe that several reactions of the spectinabilin6 and methylmalonyl-CoA7 pathway (responsible for the production of the precursor of our desired compound) have a higher metabolic flux in the mutant than in the wild-type.
Then, in order to understand which gene KO has more influence in the production of spectinabilin observed in the best mutant, we performed 5 different LMOMA simulations, each with four of the five KOs from the optimum solution. The results are present in Table 7.

Table 7 - Values of biomass and spectinabilin when we perform simulations with four of the five KOs obtained from the optimizations.
Active gene from the optimum genotype thiO cyoB PP RS16290 sdhA gcd
Biomass (h-1) 0.250 1.092 0.194 1.204 0.837
Spectinabilin Fux (mmol.(gDW.h)-1) 1.016 0.241 1.138 0.00013 0.479

The results show that the spectinabilin production is almost null if the sdhA gene is not knocked out, while in the remaining simulations the continuous activity of the other individual genes still leads to spectinabilin production. From this we can deduce that the KO of the sdhA gene is essential for the production of spectinabilin.

The gene sdhA codifies the succinate dehydrogenase flavoprotein subunit (Table 5) that is involved in the synthesis of fumarate from succinate of the tricarboxylic acid (TCA) cycle in the carbohydrate metabolism. Two of the reactions more active in the mutant strain compared with the wild-type strain present in Table 2 - the succinyl-CoA synthetase and methylmalonyl-CoA-carbonyl mutase - consume succinate to produce, respectively, succinyl-CoA and methylmalonyl-CoA. So, when the sdhA gene is inactivated, fumarate is no longer produced from succinate, creating a pool of succinate that can then be consumed by other pathways including the secondary metabolic pathways previously mentioned leading to a higher flux in the formation of (S)- methylmalonyl-CoA. Since this compound enters in the spectinabilin production pathway, an increase of (S)- methylmalonyl-CoA leads to an increased production of spectinabilin, as can also be observed by the spectinabilin pathway presenting a higher metabolic flux in the mutant strain. A similar result was observed by McCloskey et. al, when genes of the SUDCi reaction were knocked out there was increased flux out of the TCA, in E.coli12.

A second result to analyse is the KO of the gene cyoB, that codifies the cytochrome bo terminal oxidase subunit I. While its activation still allows the production of spectinabilin, the values of the desired compound are lower than those of the remaining solutions. Fluxomic studies about the KO of this gene were not found, so it is unclear its role in the mutant obtained.
The results obtained from the computational analysis will support the further development of our engineered bacteria by manipulating its metabolism in order to maximize the flux production of spectinabilin. In order to implement this solution we will start by the KO of the gene sdhA, followed up by the KO of both sdhA and cyoB.

Supplementary Material

Minimal Medium
Metabolite ID Metabolite Name Lower Bound Value Upper Bound Value
M_arg__L_e L_Arginine -2.68 999999.9
M_glu__L_e L_Glutamate -15.79 999999.9
M_glc__D_e D_Glucose -16.65 999999.9
M_pi_e Phosphate -6.90 999999.9
M_h_e H -100.0 999999.9
M_asp__L_e L_Aspartate -6.44 999999.9
M_o2_e O2 -18.50 999999.9
M_thr__L_e L_Threonine -3.45 999999.9
M_his__L_e L_Histidine -1.91 999999.9
M_ser__L_e L_Serine -6.18 999999.9
M9 medium
Metabolite ID Metabolite Name Lower Bound Value Upper Bound Value
R_EX_glc__D_e D_Glucose -10.00 0.0
R_EX_o2_e O2 -18.50 0.0
R_EX_h2o_e H2O -999999.9 999999.9
R_EX_na1_e Na -999999.9 999999.9
R_EX_co2_e CO2 -999999.9 999999.9
R_EX_cobalt2_e Co -999999.9 999999.9
R_EX_nh4_e NH4 -999999.9 999999.9
R_EX_ni2_e Ni2 -999999.9 999999.9
R_EX_so4_e SO4 -999999.9 999999.9
M_h_e H -999999.9 999999.9
M_fe3_e Fe3 -999999.9 999999.9
M_fe2_e Fe2 -999999.9 999999.9
LB medium
Metabolite ID Metabolite Name Lower Bound Value Upper Bound Value
R_EX_lys__L_e L_Lysine -5.75 999999.9
R_EX_glc__D_e D_Glucose -16.65 0.0
R_EX_val__D_e D_Valine -5.82 999999.9
R_EX_thr__L_e L_Threonine -4.37 999999.9
R_EX_phedca_e 10_Phenyldecanoic_acid -5.45 999999.9
R_EX_leu__D_e D_Leucine -2.92 999999.9
R_EX_gly_e Glycine -4.34 999999.9
R_EX_asp__L_e L_Aspartate -7.07 999999.9
R_EX_tyr__L_e L_Tyrosine -1.63 999999.9
R_EX_ser__L_e L_Serine -6.19 999999.9
R_EX_met__L_e L_Methionine -1.88 999999.9
R_EX_ile__L_e L_Isoleucine -4.48 999999.9
R_EX_glu__L_e L_Glutamate -15.79 999999.9
R_EX_arg__L_e L_Arginine -2.68 999999.9
R_EX_trp__L_e L_Tryptophan -0.71 999999.9
R_EX_pro__L_e L_Proline -9.16 999999.9
R_EX_his__L_e L_Histidine -1.91 999999.9
R_EX_cys__D_e D_Cysteine -0.64 999999.9
R_EX_ala__L_e L_Alanine -5.57 999999.9
R_EX_o2_e O2 -18.05 0.0


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