Microbial Cell (Jun 2021)

Genome, transcriptome and secretome analyses of the antagonistic, yeast-like fungus Aureobasidium pullulans to identify potential biocontrol genes

  • Maria Paula Rueda-Mejia,
  • Lukas Nägeli,
  • Stefanie Lutz,
  • Richard D. Hayes,
  • Adithi R. Varadarajan,
  • Igor V. Grigoriev,
  • Christian H. Ahrens,
  • Florian M. Freimoser

DOI
https://doi.org/10.15698/mic2021.08.757
Journal volume & issue
Vol. 8, no. 8
pp. 184 – 202

Abstract

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Aureobasidium pullulans is an extremotolerant, cosmopolitan yeast-like fungus that successfully colonises vastly different ecological niches. The species is widely used in biotechnology and successfully applied as a commercial biocontrol agent against postharvest diseases and fireblight. However, the exact mechanisms that are responsible for its antagonistic activity against diverse plant pathogens are not known at the molecular level. Thus, it is difficult to optimise and improve the biocontrol applications of this species. As a foundation for elucidating biocontrol mechanisms, we have de novo assembled a high-quality reference genome of a strongly antagonistic A. pullulans strain, performed dual RNA-seq experiments, and analysed proteins secreted during the interaction with the plant pathogen Fusarium oxysporum. Based on the genome annotation, potential biocontrol genes were predicted to encode secreted hydrolases or to be part of secondary metabolite clusters (e.g., NRPS-like, NRPS, T1PKS, terpene, and β-lactone clusters). Transcriptome and secretome analyses defined a subset of 79 A. pullulans genes (among the 10,925 annotated genes) that were transcriptionally upregulated or exclusively detected at the protein level during the competition with F. oxysporum. These potential biocontrol genes comprised predicted secreted hydrolases such as glycosylases, esterases, and proteases, as well as genes encoding enzymes, which are predicted to be involved in the synthesis of secondary metabolites. This study highlights the value of a sequential approach starting with genome mining and consecutive transcriptome and secretome analyses in order to identify a limited number of potential target genes for detailed, functional analyses.

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