A quantitative image analysis pipeline for the characterization of filamentous fungal morphologies as a tool to uncover targets for morphology engineering: a case study using aplD in Aspergillus niger

Abstract

Abstract Background Fungal fermentation is used to produce a diverse repertoire of enzymes, chemicals, and drugs for various industries. During submerged cultivation, filamentous fungi form a range of macromorphologies, including dispersed mycelia, clumped aggregates, or pellets, which have critical implications for rheological aspects during fermentation, gas/nutrient transfer, and, thus, product titres. An important component of strain engineering efforts is the ability to quantitatively assess fungal growth phenotypes, which will drive novel leads for morphologically optimized production strains. Results In this study, we developed an automated image analysis pipeline to quantify the morphology of pelleted and dispersed growth (MPD) which rapidly and reproducibly measures dispersed and pelleted macromorphologies from any submerged fungal culture. It (i) enables capture and analysis of several hundred images per user/day, (ii) is designed to quantitatively assess heterogeneous cultures consisting of dispersed and pelleted forms, (iii) gives a quantitative measurement of culture heterogeneity, (iv) automatically generates key Euclidian parameters for individual fungal structures including particle diameter, aspect ratio, area, and solidity, which are also assembled into a previously described dimensionless morphology number MN, (v) has an in-built quality control check which enables end-users to easily confirm the accuracy of the automated calls, and (vi) is easily adaptable to user-specified magnifications and macromorphological definitions. To concomitantly provide proof of principle for the utility of this image analysis pipeline, and provide new leads for morphologically optimized fungal strains, we generated a morphological mutant in the cell factory Aspergillus niger based on CRISPR-Cas technology. First, we interrogated a previously published co-expression networks for A. niger to identify a putative gamma-adaptin encoding gene (aplD) that was predicted to play a role in endosome cargo trafficking. Gene editing was used to generate a conditional aplD expression mutant under control of the titratable Tet-on system. Reduced aplD expression caused a hyperbranched growth phenotype and diverse defects in pellet formation with a putative increase in protein secretion. This possible protein hypersecretion phenotype could be correlated with increased dispersed mycelia, and both decreased pellet diameter and MN. Conclusion The MPD image analysis pipeline is a simple, rapid, and flexible approach to quantify diverse fungal morphologies. As an exemplar, we have demonstrated that the putative endosomal transport gene aplD plays a crucial role in A. niger filamentous growth and pellet formation during submerged culture. This suggests that endocytic components are underexplored targets for engineering fungal cell factories.

Keywords

• Morphology engineering
• Aspergillus niger
• Image analysis
• Pellet
• Dispersed mycelia
• Polar growth