Abstract:Over 100 genome-scale metabolic networks (GSMNs) have been published in recent years and widely used for phenotype prediction and pathway design. However, GSMNs for a specific organism reconstructed by different research groups usually produce inconsistent simulation results, which makes it difficult to use the GSMNs for precise optimal pathway design. Therefore, it is necessary to compare and identify the discrepancies among networks and build a consensus metabolic network for an organism. Here we proposed a … Show more
“…This signified cell 'growth', and indicated that the model was performing as expected for a biological system. The cell biomass composition and compartment information were derived from two available models of other Pseudomonads species: P. putida [27][28][29] and P. stutzeri 30 (see Methods).…”
Section: Developing An Integrated Genomic-transcriptomic-metabolomic mentioning
Understanding the adaptive responses of individual bacterial strains is crucial for microbiome engineering approaches that introduce new functionalities into complex microbiomes, such as xenobiotic compound metabolism for soil bioremediation. Adaptation requires metabolic reprogramming of the cell, which can be captured by multi-omics, but this data remains formidably challenging to interpret and predict. Here we present a new approach that combines genome-scale metabolic modeling with transcriptomics and exometabolomics, both of which are common tools for studying dynamic population behavior. As a realistic demonstration, we developed a genome-scale model of Pseudomonas veronii 1YdBTEX2, a candidate bioaugmentation agent for accelerated metabolism of mono-aromatic compounds in soil microbiomes, while simultaneously collecting experimental data of P. veronii metabolism during growth phase transitions. Predictions of the P. veronii growth rates and specific metabolic processes from the integrated model closely matched experimental observations. We conclude that integrative and network-based analysis can help build predictive models that accurately capture bacterial adaptation responses. Further development and testing of such models may considerably improve the successful establishment of bacterial inoculants in more complex systems.npj Systems Biology and Applications (2020) 6:1 ; https://doi.
“…This signified cell 'growth', and indicated that the model was performing as expected for a biological system. The cell biomass composition and compartment information were derived from two available models of other Pseudomonads species: P. putida [27][28][29] and P. stutzeri 30 (see Methods).…”
Section: Developing An Integrated Genomic-transcriptomic-metabolomic mentioning
Understanding the adaptive responses of individual bacterial strains is crucial for microbiome engineering approaches that introduce new functionalities into complex microbiomes, such as xenobiotic compound metabolism for soil bioremediation. Adaptation requires metabolic reprogramming of the cell, which can be captured by multi-omics, but this data remains formidably challenging to interpret and predict. Here we present a new approach that combines genome-scale metabolic modeling with transcriptomics and exometabolomics, both of which are common tools for studying dynamic population behavior. As a realistic demonstration, we developed a genome-scale model of Pseudomonas veronii 1YdBTEX2, a candidate bioaugmentation agent for accelerated metabolism of mono-aromatic compounds in soil microbiomes, while simultaneously collecting experimental data of P. veronii metabolism during growth phase transitions. Predictions of the P. veronii growth rates and specific metabolic processes from the integrated model closely matched experimental observations. We conclude that integrative and network-based analysis can help build predictive models that accurately capture bacterial adaptation responses. Further development and testing of such models may considerably improve the successful establishment of bacterial inoculants in more complex systems.npj Systems Biology and Applications (2020) 6:1 ; https://doi.
“…During the course of model reconstruction, GSMM for P. putida Kt2440 was chosen as the basis as both P. putida Kt2440 and P. fluorescens use Entner–Doudoroff (ED) pathway for glycolysis . When catechol was selected as the substrate, both strains take β‐ketoadipate route for catechol degradation .…”
Section: Methodsmentioning
confidence: 99%
“…The GSMM can be used to predict growth phenotype and analyze network properties and interpretation of experimental data . It also provides background information for metabolic engineering strategies and metabolic environment modification .…”
With the versatile metabolic diversity, Pseudomonas fluorescens is a potential candidate in petroleum aromatic hydrocarbon (PAH) bioremediation. Genome‐scale metabolic model (GSMM) can provide systematic information to guide the development of metabolic engineering strategy to improve microbial activity. In this study, a GSMM for P. fluorescens SBW25 was reconstructed, which is termed as lCW1057. The reconstruction was based on automatic reannotation and manual curation. The periplasmic compartment was constructed to better represent the proton gradient profile. The reconstructed proton transport chain has a P/O (ATP generated per atom oxygen consumed by the respiratory chain) ratio of 11/8. Flux balance analysis (FBA) was performed to explore the whole‐cell metabolic flow. The model suggested that instead of Embden–Meyerhof–Parnas pathway, Entner–Doudoroff pathway was used in glycolytic metabolism of P. fluorescens, indicating that the growth of P. fluorescens is more energy dependent. Furthermore, P. fluorescens can use nitrate as the terminal electron acceptor for the glucose metabolism. The β‐ketoadipate pathway was involved in catechol metabolism. The uptake of oxygen is mandatory for the aromatic ring cleavage. The in silico and in vitro maximum specific growth rate was compared, resulting in 10% difference when catechol was used as the sole carbon source.
“…Acetate uptake was considered via acetyl-CoA synthase (ACS) consuming 2 mol of ATP per mol acetate catabolized [58]. The ATP synthesized from NADH and FADH 2 via oxidative phosphorylation in the respiratory chain was estimated assuming a P/O ratio of 1.875 for NADH [48,98,99] and 1.0 for FADH 2 [100], respectively. The ATP demand was calculated by adding up the requirements for anabolism estimated from biomass composition, observed specific growth rates, and non-growth associated maintenance (NGAM) needs [48,98,99].…”
Pseudomonas aeruginosa is an opportunistic human pathogen, particularly noted for causing infections in the lungs of people with cystic fibrosis (CF). Previous studies have shown that the gene expression profile of P. aeruginosa appears to converge towards a common metabolic program as the organism adapts to the CF airway environment. However, at a systems level, we still have only a limited understanding of how these transcriptional changes impact on metabolic flux. To address this, we analysed the transcriptome, proteome and fluxome of P. aeruginosa grown on glycerol or acetate. These carbon sources were chosen because they are the primary breakdown products of airway surfactant, phosphatidylcholine, which is known to be a major carbon source for P. aeruginosa in the CF airways. We show that the flux of carbon through central metabolism is radically different on each carbon source. For example, the newly-recognised EDEMP cycle plays an important role in supplying NADPH during growth on glycerol. By contrast, the EDEMP cycle is attenuated during growth on acetate, and instead, NADPH is primarily supplied by the isocitrate dehydrogenase(s)-catalyzed reaction. Perhaps more importantly, our proteomic and transcriptomic analyses reveal a global remodelling of gene expression during growth on the different carbon sources, with unanticipated impacts on aerobic denitrification, electron transport chain architecture, and the redox economy of the cell. Collectively, these data highlight the remarkable metabolic plasticity of P. aeruginosa; a plasticity which allows the organism to seamlessly segue between different carbon sources, maximising the energetic yield from each.
Importance
Pseudomonas aeruginosa is an opportunistic human pathogen, well-known for causing infections in the airways of people with cystic fibrosis. Although it is clear that P. aeruginosa is metabolically well-adapted to life in the CF lung, little is currently known about how the organism metabolises the nutrients available in the airways. In this work, we use a combination of gene expression and isotope tracer (“fluxomic”) analyses to find out exactly where the input carbon goes during growth on two CF-relevant carbon sources, acetate and glycerol (derived from the breakdown of lung surfactant). We find that carbon is routed (“fluxed”) through very different pathways during growth on these substrates, and that this is accompanied by an unexpected remodelling of the cell’s electron transfer pathways. Having access to this “blueprint” is important because the metabolism of P. aeruginosa is increasingly being recognised as a target for the development of much-needed antimicrobial agents.
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