Improved annotation through genome-scale metabolic modeling of Aspergillus oryzae

<p>Abstract</p> <p>Background</p> <p>Since ancient times the filamentous fungus <it>Aspergillus oryzae </it>has been used in the fermentation industry for the production of fermented sauces and the production of industrial enzymes. Recently, the genome sequence of <it>A. oryzae </it>with 12,074 annotated genes was released but the number of hypothetical proteins accounted for more than 50% of the annotated genes. Considering the industrial importance of this fungus, it is therefore valuable to improve the annotation and further integrate genomic information with biochemical and physiological information available for this microorganism and other related fungi. Here we proposed the gene prediction by construction of an <it>A. oryzae </it>Expressed Sequence Tag (EST) library, sequencing and assembly. We enhanced the function assignment by our developed annotation strategy. The resulting better annotation was used to reconstruct the metabolic network leading to a genome scale metabolic model of <it>A. oryzae</it>.</p> <p>Results</p> <p>Our assembled EST sequences we identified 1,046 newly predicted genes in the <it>A. oryzae </it>genome. Furthermore, it was possible to assign putative protein functions to 398 of the newly predicted genes. Noteworthy, our annotation strategy resulted in assignment of new putative functions to 1,469 hypothetical proteins already present in the <it>A. oryzae </it>genome database. Using the substantially improved annotated genome we reconstructed the metabolic network of <it>A. oryzae</it>. This network contains 729 enzymes, 1,314 enzyme-encoding genes, 1,073 metabolites and 1,846 (1,053 unique) biochemical reactions. The metabolic reactions are compartmentalized into the cytosol, the mitochondria, the peroxisome and the extracellular space. Transport steps between the compartments and the extracellular space represent 281 reactions, of which 161 are unique. The metabolic model was validated and shown to correctly describe the phenotypic behavior of <it>A. oryzae </it>grown on different carbon sources.</p> <p>Conclusion</p> <p>A much enhanced annotation of the <it>A. oryzae </it>genome was performed and a genome-scale metabolic model of <it>A. oryzae </it>was reconstructed. The model accurately predicted the growth and biomass yield on different carbon sources. The model serves as an important resource for gaining further insight into our understanding of <it>A. oryzae </it>physiology.</p

Identification of Important Factors Affecting Use of Digital Individualised Coaching and Treatment of Type 2 Diabetes in General Practice: A Qualitative Feasibility Study

Most type 2 diabetes patients are treated in general practice and there is a need of developing and implementing efficient lifestyle interventions. eHealth interventions have shown to be effective in promoting a healthy lifestyle. The purpose of this study was to test the feasibility, including the identification of factors of importance, when offering digital lifestyle coaching to type 2 diabetes patients in general practice. We conducted a qualitative feasibility study with focus group interviews in four general practices. We identified two overall themes and four subthemes: (1) the distribution of roles and lifestyle interventions in general practice (subthemes: external and internal distribution of roles) and (2) the pros and cons for digital lifestyle interventions in general practice (subthemes: access to real life data and change in daily routines). We conclude that for digital lifestyle coaching to be feasible in a general practice setting, it was of great importance that the general practitioners and practice nurses knew the role and content of the intervention. In general, there was a positive attitude in the general practice setting towards referring type 2 diabetes patients to digital lifestyle intervention if it was easy to refer the patients and if easily understandable and accessible feedback was implemented into the electronic health record. It was important that the digital lifestyle intervention was flexible and offered healthcare providers in general practice an opportunity to follow the type 2 diabetes patient closely