BackgroundThe ecological niche occupied by a fungal species, its pathogenicity and its usefulness as a microbial cell factory to a large degree depends on its secretome. Protein secretion usually requires the presence of a N-terminal signal peptide (SP) and by scanning for this feature using available highly accurate SP-prediction tools, the fraction of potentially secreted proteins can be directly predicted. However, prediction of a SP does not guarantee that the protein is actually secreted and current in silico prediction methods suffer from gene-model errors introduced during genome annotation.ResultsA majority rule based classifier that also evaluates signal peptide predictions from the best homologs of three neighbouring Aspergillus species was developed to create an improved list of potential signal peptide containing proteins encoded by the Aspergillus niger genome. As a complement to these in silico predictions, the secretome associated with growth and upon carbon source depletion was determined using a shotgun proteomics approach. Overall, some 200 proteins with a predicted signal peptide were identified to be secreted proteins. Concordant changes in the secretome state were observed as a response to changes in growth/culture conditions. Additionally, two proteins secreted via a non-classical route operating in A. niger were identified.ConclusionsWe were able to improve the in silico inventory of A. niger secretory proteins by combining different gene-model predictions from neighbouring Aspergilli and thereby avoiding prediction conflicts associated with inaccurate gene-models. The expected accuracy of signal peptide prediction for proteins that lack homologous sequences in the proteomes of related species is 85%. An experimental validation of the predicted proteome confirmed in silico predictions.

Proteolytic degradation by host proteases is one of the key issues in the application of filamentous fungi for non-fungal protein production. In this study the influence of several environmental factors on the production of extracellular proteases of Aspergillus niger was investigated systematically in controlled batch cultures. Of all factors investigated in a series of initial screening experiments, culture pH and nitrogen concentration in particular strongly affected extracellular protease activities. For instance, at a culture pH of 4, protease activity was higher than at pH 5, and protease activity increased with increasing concentrations of ammonium as nitrogen source. Interestingly, an interdependence was observed for several of the factors studied. These possible interaction effects were investigated further using a full factorial experimental design. Amongst others, the results showed a clear interaction effect between nitrogen source and nitrogen concentration. Based on the observed interactions, the selection of environmental factors to reduce protease activity is not straightforward, as unexpected antagonistic or synergistic effects occur. Furthermore, not only were the effects of the process parameters on maximum protease activity investigated, but five other protease-related phenotypes were studied as well, such as maximum specific protease activity and maximum protease productivity. There were significant differences in the effect of the environmental parameters on the various protease-related phenotypes. For instance, pH significantly affected final levels of protease activity, but not protease productivity. The results obtained in this study are important for the optimization of A. niger for protein production.

For the optimization of microbial production processes, the choice of the quantitative phenotype to be optimized is crucial. For instance, for the optimization of product formation, either product concentration or productivity can be pursued, potentially resulting in different targets for strain improvement. The choice of a quantitative phenotype is highly relevant for classical improvement approaches, and even more so for modern systems biology approaches. In this study, the information content of a metabolomics dataset was determined with respect to different quantitative phenotypes related to the formation of specific products. To this end, the production of two industrially relevant products by Aspergillus niger was evaluated: (i) the enzyme glucoamylase, and (ii) the more complex product group of secreted proteases, consisting of multiple enzymes. For both products, six quantitative phenotypes associated with activity and productivity were defined, also taking into account different time points of sampling during the fermentation. Both linear and nonlinear relationships between the metabolome data and the different quantitative phenotypes were considered. The multivariate data analysis tool partial least-squares (PLS) was used to evaluate the information content of the datasets for all the different quantitative phenotypes defined. Depending on the product studied, different quantitative phenotypes were found to have the highest information content in specific metabolomics datasets. A detailed analysis of the metabolites that showed strong correlation with these quantitative phenotypes revealed that various sugar derivatives correlated with glucoamylase activity. For the reduction of protease activity, mainly as-yet-unidentified compounds correlated.