Dataset Information

Mapping high-growth phenotypes in the flux space of microbial metabolism.

ABSTRACT: Experimental and empirical observations on cell metabolism cannot be understood as a whole without their integration into a consistent systematic framework. However, the characterization of metabolic flux phenotypes is typically reduced to the study of a single optimal state, such as maximum biomass yield that is by far the most common assumption. Here, we confront optimal growth solutions to the whole set of feasible flux phenotypes (FFPs), which provides a benchmark to assess the likelihood of optimal and high-growth states and their agreement with experimental results. In addition, FFP maps are able to uncover metabolic behaviours, such as aerobic fermentation accompanying exponential growth on sugars at nutrient excess conditions, that are unreachable using standard models based on optimality principles. The information content of the full FFP space provides us with a map to explore and evaluate metabolic behaviour and capabilities, and so it opens new avenues for biotechnological and biomedical applications.

SUBMITTER: Guell O

PROVIDER: S-EPMC4614465 | biostudies-literature | 2015 Sep

REPOSITORIES: biostudies-literature

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Mapping high-growth phenotypes in the flux space of microbial metabolism.

Güell Oriol O Massucci Francesco Alessandro FA Font-Clos Francesc F Sagués Francesc F Serrano M Ángeles MÁ

Journal of the Royal Society, Interface 20150901 110

Experimental and empirical observations on cell metabolism cannot be understood as a whole without their integration into a consistent systematic framework. However, the characterization of metabolic flux phenotypes is typically reduced to the study of a single optimal state, such as maximum biomass yield that is by far the most common assumption. Here, we confront optimal growth solutions to the whole set of feasible flux phenotypes (FFPs), which provides a benchmark to assess the likelihood of ...[more]

PMID: 26289659

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Project description:Genome sequencing dramatically increased our ability to understand cellular response to perturbation. Integrating system-wide measurements such as gene expression with networks of protein-protein interactions and transcription factor binding revealed critical insights into cellular behavior. However, the potential of systems biology approaches is limited by difficulties in integrating metabolic measurements across the functional levels of the cell despite their being most closely linked to cellular phenotype. To address this limitation, we developed a model-based approach to correlate mRNA and metabolic flux data that combines information from both interaction network models and flux determination models. We started by quantifying 5,764 mRNAs, 54 metabolites, and 83 experimental (13)C-based reaction fluxes in continuous cultures of yeast under stress in the absence or presence of global regulator Gcn4p. Although mRNA expression alone did not directly predict metabolic response, this correlation improved through incorporating a network-based model of amino acid biosynthesis (from r = 0.07 to 0.80 for mRNA-flux agreement). The model provides evidence of general biological principles: rewiring of metabolic flux (i.e., use of different reaction pathways) by transcriptional regulation and metabolite interaction density (i.e., level of pairwise metabolite-protein interactions) as a key biosynthetic control determinant. Furthermore, this model predicted flux rewiring in studies of follow-on transcriptional regulators that were experimentally validated with additional (13)C-based flux measurements. As a first step in linking metabolic control and genetic regulatory networks, this model underscores the importance of integrating diverse data types in large-scale cellular models. We anticipate that an integrated approach focusing on metabolic measurements will facilitate construction of more realistic models of cellular regulation for understanding diseases and constructing strains for industrial applications.

Project description:Higher dietary energy is often used to achieve better animal performance in mutton sheep production. Notably, changing the diet formula affects rumen fermentation and the microbiota of ruminants. In this study, we investigated the effect of dietary energy on rumen fermentation and ruminal microbiota in fattening sheep. Fifteen 2-month-old white-headed Suffolk sheep (♂) × Hu sheep (♀) crossbred lambs were randomly divided into three treatments based on the dietary energy of the feeds fed: 8.67 MJ/kg (Low energy (LE); n = 5), 10.38 MJ/kg (standard energy (CON); n = 5), and 12.31 MJ/kg (high energy (HE); n = 5) groups. After 70 days of feeding, sheep were slaughtered and the ruminal fluids were collected and analyzed to determine fermentation parameters. Microbiota was determined using metagenomics sequencing. Notably, the microbial cell protein (MCP) and butyric acid concentrations were significantly high in the HE group. Metagenomic sequencing revealed that ACE and Chao indexes of the HE group were significantly decreased. Four genera among the major classified taxa across all the kingdoms differed in relative abundance in the three dietary energy levels. The relative abundances of Prevotella_brevis, Succiniclasticum_ruminis, Prevotellace-ae_bacterium, and Lachnospiraceae_bacterium were significantly correlated with rumen fermentation. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis further revealed that a high-energy diet increased lipid metabolism of microbiota. The Carbohydrate Active enzymes (CAZy) gene, which participates in energy metabolism, was upregulated, while genes regulating plant cell wall degradation were downregulated in the HE group. These results suggest that a high-energy diet had minimal influence on the rumen fermentation pattern but altered the composition of the rumen microbiota, enhancing microbial lipid metabolism and limiting crude fiber metabolism. The findings of this study provide scientific evidence of the effect of dietary energy on ruminant fermentation and fattening sheep production.

Dataset Information

Mapping high-growth phenotypes in the flux space of microbial metabolism.

Publications

Mapping high-growth phenotypes in the flux space of microbial metabolism.

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