A Library of Aspergillus niger Chassis Strains for Morphology Engineering Connects Strain Fitness and Filamentous Growth With Submerged Macromorphology

Abstract

Submerged fermentation using filamentous fungal cell factories is used to produce a diverse portfolio of useful molecules, including food, medicines, enzymes, and platform chemicals. Depending on strain background and abiotic culture conditions, different macromorphologies are formed during fermentation, ranging from dispersed hyphal fragments to approximately spherical pellets several millimetres in diameter. These macromorphologies are known to have a critical impact on product titres and rheological performance of the bioreactor. Pilot productivity screens in different macromorphological contexts is technically challenging, time consuming, and thus a significant limitation to achieving maximum product titres. To address this bottleneck, we developed a library of conditional expression mutants in the organic, protein, and secondary metabolite cell factory Aspergillus niger. Thirteen morphology-associated genes transcribed during fermentation were placed via CRISPR-Cas9 under control of a synthetic Tet-on gene switch. Quantitative analysis of submerged growth reveals that these strains have distinct and titratable macromorphologies for use as chassis during strain engineering programs. We also used this library as a tool to quantify how pellet formation is connected with strain fitness and filamentous growth. Using multiple linear regression modelling, we predict that pellet formation is dependent largely on strain fitness, whereas pellet Euclidian parameters depend on fitness and hyphal branching. Finally, we have shown that conditional expression of the putative kinase encoding gene pkh2 can decouple fitness, dry weight, pellet macromorphology, and culture heterogeneity. We hypothesize that further analysis of this gene product and the cell wall integrity pathway in which it is embedded will enable more precise engineering of A. niger macromorphology in future.

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

“…Rational design and control of optimal hyperproducing strains will require integrated understanding of how these complex factors are interconnected. In a proof of principle study, we previously assessed how filamentous hyphal morphology impacts macromorphological development in the organic acid and protein producing fungus Aspergillus niger [ 24 ]. By quantifying filamentous growth and submerged macromorphology of conditional expression mutants, it was possible to use multiple linear regression modelling to predict that changes in strain hyphal tip number, but not length, did indeed cause measurable impacts in macromorphological development.…”

“…By quantifying filamentous growth and submerged macromorphology of conditional expression mutants, it was possible to use multiple linear regression modelling to predict that changes in strain hyphal tip number, but not length, did indeed cause measurable impacts in macromorphological development. Crucially, this approach analysed filamentous growth and macromorphology across multiple genetic backgrounds, which enables greater confidence when compared to similar experiments using a single isolate [ 24 ].…”

The expanding CRISPR toolbox for natural product discovery and engineering in filamentous fungi

“…niger was developed using CRISPR/Cas integration for morphology engineering, which is known to impact the yield of some compounds. 131 CRISPR/Cas protocols optimised for microtiter plates and robotics could enable the construction of larger arrayed libraries by scaled down transformation reactions and increases to throughput. 132 Although pooled assays could be simpler to scale up, sorting chemical phenotypes can be troublesome.…”

Quantitative phenotypic screens of Aspergillus niger mutants in solid and liquid culture

“…Note, that when multiple strains are incorporated into such data matrix, correlations e.g., between cell wall perturbation and pellet diameter, become more robust and easier to delineate. 2 …”