bioRxivEvolutionary BiologyPreprint
Evolutionary and biological mechanisms underpinning chitin degradation in aquatic fungi
N. Chrismas, K. Bird, D. Laundon, P. Hesketh-Best, C. Lieng, M. Cunliffe
Abstract
Fungal biology underpins major processes in ecosystems. The Chytridiomycota (chytrids) is a group of early-diverging fungi, many of which function in ecosystems as saprotrophs processing high molecular weight biopolymers, however the mechanisms underpinning chytrid saprotrophy are poorly understood. Genome sequences from representatives across the group and the use of model chytrids offers the potential to determine new insights into their evolution. In this study, we focused on the biology underpinning chitin saprotrophy, a common ecosystem function of aquatic chytrids. The genomes of chitinophilic chytrids have expanded inventories of glycoside hydrolase genes responsible for chitin processing, complemented with bacteria-like chitin-binding modules (CBMs) that are absent in other chytrids. In the model chitinophilic saprotroph Rhizoclosmatium globosum JEL800, the expanded repertoire of chitinase genes is diverse and almost half were detected as proteins in the secretome when grown with chitin. Predicted models of the secreted chitinases indicate a range of active site sizes and domain configurations. We propose that increased diversity of secreted chitinases is an adaptive strategy that facilitates chitin degradation in the complex heterologous organic matrix of the arthropod exoskeleton. Free swimming R. globosum JEL800 zoospores are chemotactic to the chitin monomer N-acetylglucosamine and accelerate zoospore development when grown with chitin. Our study sheds light on the underpinning biology and evolutionary mechanisms that have supported the saprotrophic niche expansion of some chytrids to utilise lucrative chitin-rich particles in aquatic ecosystems and is a demonstration of the adaptive capability of this successful fungal group.
- assay: whole-genome sequencing
- research focus: chromatin & epigenetic regulation
- approach: statistical model
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