Construction and Validation of a Genome-Scale Metabolic Network of Thermotoga sp. Strain RQ7.

Thermotoga are anaerobic hyperthermophiles that have a deep lineage to the last universal ancestor and produce biological hydrogen gas accompanying cell growth. In recent years, systems-level approaches have been used to elucidate their metabolic capacities, by integrating mathematical modeling and experimental results. To assist biochemical engineering studies of T. sp. strain RQ7, this work aims at building a metabolic model of the bacterium that quantitatively simulates its metabolism at the genome scale. The constructed model, RQ7_iJG408, consists of 408 genes, 692 reactions, and 538 metabolites. Constraint-based flux balance analyses were used to simulate cell growth in both the complex and defined media. Quantitative comparison of the predicted and measured growth rates resulted in good agreements. This model serves as a foundation for an integrated biochemical description of T. sp. strain RQ7. It is a useful tool in designing growth media, identifying metabolic engineering strategies, and exploiting the physiological potentials of this biotechnologically significant organism.

Reconstructing organisms in silico: genome-scale models and their emerging applications.

Escherichia coli is considered to be the best-known microorganism given the large number of published studies detailing its genes, its genome and the biochemical functions of its molecular components. This vast literature has been systematically assembled into a reconstruction of the biochemical reaction networks that underlie E. coli's functions, a process which is now being applied to an increasing number of microorganisms. Genome-scale reconstructed networks are organized and systematized knowledge bases that have multiple uses, including conversion into computational models that interpret and predict phenotypic states and the consequences of environmental and genetic perturbations. These genome-scale models (GEMs) now enable us to develop pan-genome analyses that provide mechanistic insights, detail the selection pressures on proteome allocation and address stress phenotypes. In this Review, we first discuss the overall development of GEMs and their applications. Next, we review the evolution of the most complete GEM that has been developed to date: the E. coli GEM. Finally, we explore three emerging areas in genome-scale modelling of microbial phenotypes: collections of strain-specific models, metabolic and macromolecular expression models, and simulation of stress responses.

Effect of Cultivation Parameters on Fermentation and Hydrogen Production in the Phylum Thermotogae.

The phylum Thermotogae is composed of a single class (Thermotogae), 4 orders (Thermotogales, Kosmotogales, Petrotogales, Mesoaciditogales), 5 families (Thermatogaceae, Fervidobacteriaceae, Kosmotogaceae, Petrotogaceae, Mesoaciditogaceae), and 13 genera. They have been isolated from extremely hot environments whose characteristics are reflected in the metabolic and phenotypic properties of the Thermotogae species. The metabolic versatility of Thermotogae members leads to a pool of high value-added products with application potentials in many industry fields. The low risk of contamination associated with their extreme culture conditions has made most species of the phylum attractive candidates in biotechnological processes. Almost all members of the phylum, especially those in the order Thermotogales, can produce bio-hydrogen from a variety of simple and complex sugars with yields close to the theoretical Thauer limit of 4 mol H2/mol consumed glucose. Acetate, lactate, and L-alanine are the major organic end products. Thermotagae fermentation processes are influenced by various factors, such as hydrogen partial pressure, agitation, gas sparging, culture/headspace ratio, inoculum, pH, temperature, nitrogen sources, sulfur sources, inorganic compounds, metal ions, etc. Optimization of these parameters will help to fully unleash the biotechnological potentials of Thermotogae and promote their applications in industry. This article gives an overview of how these operational parameters could impact Thermotogae fermentation in terms of sugar consumption, hydrogen yields, and organic acids production.