Dataset Information

Metabolic modeling of common Escherichia coli strains in human gut microbiome.

ABSTRACT: The recent high-throughput sequencing has enabled the composition of Escherichia coli strains in the human microbial community to be profiled en masse. However, there are two challenges to address: (1) exploring the genetic differences between E. coli strains in human gut and (2) dynamic responses of E. coli to diverse stress conditions. As a result, we investigated the E. coli strains in human gut microbiome using deep sequencing data and reconstructed genome-wide metabolic networks for the three most common E. coli strains, including E. coli HS, UTI89, and CFT073. The metabolic models show obvious strain-specific characteristics, both in network contents and in behaviors. We predicted optimal biomass production for three models on four different carbon sources (acetate, ethanol, glucose, and succinate) and found that these stress-associated genes were involved in host-microbial interactions and increased in human obesity. Besides, it shows that the growth rates are similar among the models, but the flux distributions are different, even in E. coli core reactions. The correlations between human diabetes-associated metabolic reactions in the E. coli models were also predicted. The study provides a systems perspective on E. coli strains in human gut microbiome and will be helpful in integrating diverse data sources in the following study.

SUBMITTER: Gao YD

PROVIDER: S-EPMC4122010 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature

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Metabolic modeling of common Escherichia coli strains in human gut microbiome.

Gao Yue-Dong YD Zhao Yuqi Y Huang Jingfei J

BioMed research international 20140713

The recent high-throughput sequencing has enabled the composition of Escherichia coli strains in the human microbial community to be profiled en masse. However, there are two challenges to address: (1) exploring the genetic differences between E. coli strains in human gut and (2) dynamic responses of E. coli to diverse stress conditions. As a result, we investigated the E. coli strains in human gut microbiome using deep sequencing data and reconstructed genome-wide metabolic networks for the thr ...[more]

PMID: 25126572

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Project description:The gut microbiome is a complex microbial community that has a significant influence on the host. Microbial interactions in the gut are mediated by dietary substrates, especially complex polysaccharides. In this environment, breakdown products from larger carbohydrates and short chain fatty acids are commonly shared among gut microbes. Understanding the forces that guide microbiome development and composition is important to determine its role in health and in the intervention of the gut microbiome as a therapeutic tool. Recently, modeling approaches such as genome-scale models and time-series analyses have been useful to predict microbial interactions. In this study, a bottom-up approach was followed to develop a mathematical model based on microbial growth equations that incorporate metabolic sharing and inhibition. The model was developed using experimental in vitro data from a system comprising four microorganisms of the infant gut microbiome (Bifidobacterium longum subsp. infantis, Lactobacillus acidophilus, Escherichia coli, and Bacteroides vulgatus), one substrate (fructooligosaccharides, FOS), and evaluating two metabolic products (acetate and lactate). After parameter optimization, the model accurately predicted bacterial abundance in co-cultures from mono-culture data. In addition, a good correlation was observed between the experimental data with predicted FOS consumption and acid production. B. infantis and L. acidophilus were dominant under these conditions. Further model validation included cultures with the four-species in a bioreactor using FOS. The model was able to predict the predominance of the two aforementioned species, as well as depletion of acetate and lactate. Finally, the model was tested for parameter identifiability and sensitivity. These results suggest that variations in microbial abundance and activities in the infant gut were mainly explained by metabolic interactions, and could be properly modeled using Monod kinetics with metabolic interactions. The model could be scaled to include data from larger consortia, or be applied to microbial communities where sharing metabolic resources is important in shaping bacterial abundance. Moreover, the model could be useful in designing microbial consortia with desired properties such as higher acid production.

Project description:BackgroundThe rational design of L-phenylalanine (L-Phe) overproducing microorganisms has been successfully achieved by combining different genetic strategies such as inactivation of the phosphoenolpyruvate: phosphotransferase transport system (PTS) and overexpression of key genes (DAHP synthase, transketolase and chorismate mutase-prephenate dehydratase), reaching yields of 0.33 (g-Phe/g-Glc), which correspond to 60% of theoretical maximum. Although genetic modifications introduced into the cell for the generation of overproducing organisms are specifically targeted to a particular pathway, these can trigger unexpected transcriptional responses of several genes. In the current work, metabolic transcription analysis (MTA) of both L-Phe overproducing and non-engineered strains using Real-Time PCR was performed, allowing the detection of transcriptional responses to PTS deletion and plasmid presence of genes related to central carbon metabolism. This MTA included 86 genes encoding enzymes of glycolysis, gluconeogenesis, pentoses phosphate, tricarboxylic acid cycle, fermentative and aromatic amino acid pathways. In addition, 30 genes encoding regulatory proteins and transporters for aromatic compounds and carbohydrates were also analyzed.ResultsMTA revealed that a set of genes encoding carbohydrate transporters (galP, mglB), gluconeogenic (ppsA, pckA) and fermentative enzymes (ldhA) were significantly induced, while some others were down-regulated such as ppc, pflB, pta and ackA, as a consequence of PTS inactivation. One of the most relevant findings was the coordinated up-regulation of several genes that are exclusively gluconeogenic (fbp, ppsA, pckA, maeB, sfcA, and glyoxylate shunt) in the best PTS- L-Phe overproducing strain (PB12-ev2). Furthermore, it was noticeable that most of the TCA genes showed a strong up-regulation in the presence of multicopy plasmids by an unknown mechanism. A group of genes exhibited transcriptional responses to both PTS inactivation and the presence of plasmids. For instance, acs-ackA, sucABCD, and sdhABCD operons were up-regulated in PB12 (PTS mutant that carries an arcB- mutation). The induction of these operons was further increased by the presence of plasmids in PB12-ev2. Some genes involved in the shikimate and specific aromatic amino acid pathways showed down-regulation in the L-Phe overproducing strains, might cause possible metabolic limitations in the shikimate pathway.ConclusionThe identification of potential rate-limiting steps and the detection of transcriptional responses in overproducing microorganisms may suggest "reverse engineering" strategies for the further improvement of L-Phe production strains.

Project description:BackgroundIntestinal microbiota plays an important role in the human health. It is involved in the digestion and protects the host against external pathogens. Examination of the intestinal microbiome interactions is required for understanding of the community influence on host health. Studies of the microbiome can provide insight on methods of improving health, including specific clinical procedures for individual microbial community composition modification and microbiota correction by colonizing with new bacterial species or dietary changes.Methodology/principal findingsIn this work we report an agent-based model of interactions between two bacterial species and between species and the gut. The model is based on reactions describing bacterial fermentation of polysaccharides to acetate and propionate and fermentation of acetate to butyrate. Antibiotic treatment was chosen as disturbance factor and used to investigate stability of the system. System recovery after antibiotic treatment was analyzed as dependence on quantity of feedback interactions inside the community, therapy duration and amount of antibiotics. Bacterial species are known to mutate and acquire resistance to the antibiotics. The ability to mutate was considered to be a stochastic process, under this suggestion ratio of sensitive to resistant bacteria was calculated during antibiotic therapy and recovery.Conclusion/significanceThe model confirms a hypothesis of feedbacks mechanisms necessity for providing functionality and stability of the system after disturbance. High fraction of bacterial community was shown to mutate during antibiotic treatment, though sensitive strains could become dominating after recovery. The recovery of sensitive strains is explained by fitness cost of the resistance. The model demonstrates not only quantitative dynamics of bacterial species, but also gives an ability to observe the emergent spatial structure and its alteration, depending on various feedback mechanisms. Visual version of the model shows that spatial structure is a key factor, which helps bacteria to survive and to adapt to changed environmental conditions.

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Metabolic modeling of common Escherichia coli strains in human gut microbiome.

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Metabolic modeling of common Escherichia coli strains in human gut microbiome.

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