Metabolomics as a tool for target identification in strain improvement: the influence of phenotype definition

Abstract

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.