Genome-scale metabolic modeling of P. thermoglucosidasius NCIMB 11955 reveals metabolic bottlenecks in anaerobic metabolism.

Parageobacillus thermoglucosidasius represents a thermophilic, facultative anaerobic bacterial chassis, with several desirable traits for metabolic engineering and industrial production. To further optimize strain productivity, a systems level understanding of its metabolism is needed, which can be facilitated by a genome-scale metabolic model. Here, we present p-thermo, the most complete, curated and validated genome-scale model (to date) of Parageobacillus thermoglucosidasius NCIMB 11955. It spans a total of 890 metabolites, 1175 reactions and 917 metabolic genes, forming an extensive knowledge base for P. thermoglucosidasius NCIMB 11955 metabolism. The model accurately predicts aerobic utilization of 22 carbon sources, and the predictive quality of internal fluxes was validated with previously published 13C-fluxomics data. In an application case, p-thermo was used to facilitate more in-depth analysis of reported metabolic engineering efforts, giving additional insight into fermentative metabolism. Finally, p-thermo was used to resolve a previously uncharacterised bottleneck in anaerobic metabolism, by identifying the minimal required supplemented nutrients (thiamin, biotin and iron(III)) needed to sustain anaerobic growth. This highlights the usefulness of p-thermo for guiding the generation of experimental hypotheses and for facilitating data-driven metabolic engineering, expanding the use of P. thermoglucosidasius as a high yield production platform.

The genus Geobacillus and their biotechnological potential.

The genus Geobacillus comprises a group of Gram-positive thermophilic bacteria, including obligate aerobes, denitrifiers, and facultative anaerobes that can grow over a range of 45-75°C. Originally classified as group five Bacillus spp., strains of Bacillus stearothermophilus came to prominence as contaminants of canned food and soon became the organism of choice for comparative studies of metabolism and enzymology between mesophiles and thermophiles. More recently, their catabolic versatility, particularly in the degradation of hemicellulose and starch, and rapid growth rates have raised their profile as organisms with potential for second-generation (lignocellulosic) biorefineries for biofuel or chemical production. The continued development of genetic tools to facilitate both fundamental investigation and metabolic engineering is now helping to realize this potential, for both metabolite production and optimized catabolism. In addition, this catabolic versatility provides a range of useful thermostable enzymes for industrial application. A number of genome-sequencing projects have been completed or are underway allowing comparative studies. These reveal a significant amount of genome rearrangement within the genus, the presence of large genomic islands encompassing all the hemicellulose utilization genes and a genomic island incorporating a set of long chain alkane monooxygenase genes. With G+C contents of 45-55%, thermostability appears to derive in part from the ability to synthesize protamine and spermine, which can condense DNA and raise its Tm.

PathwayBooster: a tool to support the curation of metabolic pathways.

BACKGROUND: Despite several recent advances in the automated generation of draft metabolic reconstructions, the manual curation of these networks to produce high quality genome-scale metabolic models remains a labour-intensive and challenging task. RESULTS: We present PathwayBooster, an open-source software tool to support the manual comparison and curation of metabolic models. It combines gene annotations from GenBank files and other sources with information retrieved from the metabolic databases BRENDA and KEGG to produce a set of pathway diagrams and reports summarising the evidence for the presence of a reaction in a given organism's metabolic network. By comparing multiple sources of evidence within a common framework, PathwayBooster assists the curator in the identification of likely false positive (misannotated enzyme) and false negative (pathway hole) reactions. Reaction evidence may be taken from alternative annotations of the same genome and/or a set of closely related organisms. CONCLUSIONS: By integrating and visualising evidence from multiple sources, PathwayBooster reduces the manual effort required in the curation of a metabolic model. The software is available online at http://www.theosysbio.bio.ic.ac.uk/resources/pathwaybooster/ .