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. 2024 Aug 28;25(17):9321.
doi: 10.3390/ijms25179321.

Reconstruction and Analysis of a Genome-Scale Metabolic Model of Acinetobacter lwoffii

Nan Xu  1 , Jiaojiao Zuo  1 , Chenghao Li  1 , Cong Gao  2 , Minliang Guo  1
Affiliations

Reconstruction and Analysis of a Genome-Scale Metabolic Model of Acinetobacter lwoffii

Nan Xu et al. Int J Mol Sci. .

Abstract

Acinetobacter lwoffii is widely considered to be a harmful bacterium that is resistant to medicines and disinfectants. A. lwoffii NL1 degrades phenols efficiently and shows promise as an aromatic compound degrader in antibiotic-contaminated environments. To gain a comprehensive understanding of A. lwoffii, the first genome-scale metabolic model of A. lwoffii was constructed using semi-automated and manual methods. The iNX811 model, which includes 811 genes, 1071 metabolites, and 1155 reactions, was validated using 39 unique carbon and nitrogen sources. Genes and metabolites critical for cell growth were analyzed, and 12 essential metabolites (mainly in the biosynthesis and metabolism of glycan, lysine, and cofactors) were identified as antibacterial drug targets. Moreover, to explore the metabolic response to phenols, metabolic flux was simulated by integrating transcriptomics, and the significantly changed metabolism mainly included central carbon metabolism, along with some transport reactions. In addition, the addition of substances that effectively improved phenol degradation was predicted and validated using the model. Overall, the reconstruction and analysis of model iNX811 helped to study the antimicrobial systems and biodegradation behavior of A. lwoffii.

Keywords: Acinetobacter lwoffii; drug targets; genome-scale metabolic model; phenol degradation.

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Conflict of interest statement

The authors declare that they have no competing interests.

Figures

Figure 1
Figure 1
The genome-scale model reconstruction process for A. lwoffii.
Figure 2
Figure 2
Genes, reactions, and gene-associated reactions for each metabolic subsystem of the model iNX811. Genes indicate how many genes there are in each metabolic subsystem, total reactions show how many reactions there are in each metabolic subsystem, and gene-associated reactions show which metabolic reactions have their encoding genes annotated.
Figure 3
Figure 3
The metabolic states feasible through the phenol ortho-cleavage pathway of A. lwoffii under different cultivation conditions: (A–C) Different cultivation conditions, i.e., NaAc as the sole carbon source, NaAc with 0.5 g/L phenol as carbon sources, and NaAc with 1.5 g/L phenol as carbon sources. The range of flux distributions with and without the regulatory constraints is shown in blue and in orange, respectively. (D) The phenol ortho-cleavage pathway in A. lwoffii. All of the reactions (R numbers) were from the KEGG database.
Figure 4
Figure 4
Flux comparison of three context-specific genome-scale models constrained by differential transcriptomic data. Conditions 1–3 involved using NaAc as the only carbon source, adding 0.5 g/L of phenol as a carbon source, and adding 1.5 g/L of phenol as a carbon source, respectively. GAR, phosphoribosylglycinamide; PEP, phosphoenolpyruvate.
Figure 5
Figure 5
Effects of phenol and sodium acetate on cell growth using phenotype phase-plane analysis. The Ex_C00146 and Ex_C00033(e) axes represent the phenol uptake rate and sodium acetate uptake rate, respectively. The colored legend signifies cell growth rates.
Figure 6
Figure 6
Effects of the addition of various substances on phenol degradation: (A–E) the dynamic curves of cell growth and phenol contents after adding pyruvate (B), malate (C), succinate (D), and alanine (E). The chemicals contained the same number of carbon atoms (0.02 moles). (A) Control. (F) Their breakdown rates at 20 h after introducing six amino acids.
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