Regression modelling of conditional morphogene expression links and quantifies the impact of growth rate, fitness and macromorphology with protein secretion in Aspergillus niger.

Background: Filamentous fungi are used as industrial cell factories to produce a diverse portfolio of proteins, organic acids, and secondary metabolites in submerged fermentation. Generating optimized strains for maximum product titres relies on a complex interplay of molecular, cellular, morphological, and macromorphological factors that are not yet fully understood.

Results: In this study, we generate six conditional expression mutants in the protein producing ascomycete Aspergillus niger and use them as tools to reverse engineer factors which impact total secreted protein during submerged growth.