Fig 1.
Workflow to build the metabolic model of Thauera sp. MZ1T using a semiautomatic approach.
An initial draft M-model was reconstructed using three sets of BLASTp parameters (e-value, query length, and identity percentage) from three template models present in BiGG (Escherichia coli K-12 substr. MG1655, Klebsiella pneumoniae subsp. pneumoniae MGH 78578, and Pseudomonas putida KT2440). NCBI reference sequence annotation (GenBank) was employed in GPR associations. The RAVEN and COBRA toolboxes for MATLAB were employed in the reconstruction, refinement, and validation of the model. The resulting optimized draft model and constituents of the BOF were manually curated. Protein, RNA, and DNA components of the BOF were estimated based on the total coding sequences. Disconnected metabolites were linked to the metabolic pathways using bioinformatics databases and experimental evidence. Four detailed metabolic modules were carefully added to the M-model to show specific metabolic capabilities of Thauera sp. MZ1T: 1) aromatic compound degradation under aerobic and anaerobic conditions, 2) N metabolism including denitrification (with nitric and nitrous oxide partial denitrification), oxidative phosphorylation with nitrate as electron acceptor, and DNRA, 3) PHAs and PHB production, and 4) EPS precursor production. The resulting model was validated using experimental data retrieved from the literature. The iterative model refinement process included manual curation, gap-filling, and curation under heterotrophic conditions with different oxygen concentrations depending on the experimental environments. PHB and EPS production was simulated using a set of 36 C sources under aerobic and anaerobic conditions to estimate the compounds with higher yields. The final model (iThauera861), containing 1,744 metabolites, 2,384 reactions, and 861 genes, predicted growth up to 95% of accuracy for 60 C and N substrates.
Fig 2.
Metabolic properties of Thauera sp. MZ1T represented in iThauera861.
Anaerobically (left side), iThauera861 deploys six specific pathways to degrade aromatic compounds, as depicted inside the cell diagram (from left to right): 4-chlorobenzoic acid, benzyl alcohol, p-cresol, aniline, and phenylacetic acid. These metabolites are converted into a common intermediate, benzoyl-CoA, and finally into acetyl-CoA and pyruvate as the main compounds to connect with global C metabolism. In absence of oxygen, iThauera861 employs nitrate (NO3) as the main electron acceptor, converting the NO3- into N2 through the denitrification pathway, or ammonium (NH4+) using DNRA. In the presence of oxygen (right side), iThauera861 contains specific enzymes to oxidize aromatic compounds such as benzene, toluene, benzoate, and derivatives and convert them into pyruvate. Oxygen is employed as the key electron acceptor instead of NO3-. In both conditions, the M-model can produce PHB using acetyl-CoA as the main C precursor, with higher yields of PHB biosynthesis estimated in presence of oxygen. iThauera861 contains six specific EPS biosynthetic reactions using dTDP-D-N-acetylfucosamine (brown), dTDP-L-rhamnose (purple), UDP-D-galactose (blue), and UDP-N-acetylglucosamine (orange) in different proportions.
Table 1.
Summary of the growth rates evaluated using different carbon and nitrogen sources under aerobic and anaerobic conditions.
Fig 3.
Heatmap containing the -log10 transformation of the Mann Whitney p-values for the up-regulated subsystems using sampling results of the optimal growth values under aerobic and anaerobic conditions.
The flux distributions for each aromatic C source were estimated through sampling analysis and the resulting values were grouped and averaged per subsystem. Each growth condition was compared against acetate to determine the upregulated and downregulated subsystems through the Mann Whitney U test. The p-values were plotted using -log10 transformation to identify which metabolic pathways were significantly upregulated. The eleven aerobic experimental growth conditions are displayed on the left and the seven anaerobic denitrifying conditions are shown on the right. Abbreviations: 4abz_e, 4-aminobenzoate; 4crsol_e, p-cresol; 4hbz_e, 4-hydroxybenzoate; 4hphac_e, 4-hydroxyphenylacetate; anilIne_e, aniline; bz_e, benzoate; bzalc_e, benzyl alcohol; m_xyl_e, m-xylene; pac_e, phenylacetic acid; phenol_e, phenol; tol_e, toluene.
Fig 4.
Effect of the C:N ratio and different carbon sources on production of PHB and EPS.
A Scatter plot depicting the production per C molecule and C:N ratios of PHB and EPS under aerobic condition with 36 C sources. No clear tendency can be observed from 0 to 5 using the C:N ratio as reference. With C:N ratio above 5:1 a clear increase in the yield is noticed showing the highest yields when C:N ratios are higher for PHB production. B Comparison of the yields per C molecule in the presence and absence of oxygen. Most of the yields are concentrated in the center of the plot, meaning that the yields are remarkably similar independently of the oxygen concentration. C PCA plot displaying the two first component scores of the C:N ratios considering the PCA coefficients of PHB and EPS under aerobic conditions. The PCA represents 99% of the variability of the data. Two main clusters can be identified, the cluster of the EPS (left side) and the cluster of the PHB (right side). D First two component scores of the C:N ratio PCA under aerobic conditions, considering the PCA coefficients of C sources. The compounds were grouped by their common functional groups. Interestingly, two main clusters were recognized, the common functional groups with N (left) and the compounds without N in their chemical composition (right).
Fig 5.
Reaction essentiality analysis for the production of PHB and EPSs under aerobic and anaerobic conditions using different carbon sources.
A Upset plot with 20 distinct groups of lethal reactions for PHB and six EPSs under aerobic conditions. Pie charts of the reaction subsystem distribution above each bar from the lethal reaction groups. B Upset plot with 19 diverse groups of lethal reactions for PHB and six EPSs under anaerobic conditions. Pie charts of the reaction subsystem distribution above each bar from the lethal reaction groups. C Percentage distribution of enzyme classification of lethal genes estimated through single gene deletion under aerobic condition (outer plot) and the C sources affected by the lethal reactions (inner plot). D Percentage distribution of the enzyme classification of the lethal genes estimated through single gene deletion under anaerobic condition (outer plot) and the C sources affected by the lethal reactions (inner plot).