PLoS ONE (Jan 2013)

Network-based data integration for selecting candidate virulence associated proteins in the cereal infecting fungus Fusarium graminearum.

  • Artem Lysenko,
  • Martin Urban,
  • Laura Bennett,
  • Sophia Tsoka,
  • Elzbieta Janowska-Sejda,
  • Chris J Rawlings,
  • Kim E Hammond-Kosack,
  • Mansoor Saqi

DOI
https://doi.org/10.1371/journal.pone.0067926
Journal volume & issue
Vol. 8, no. 7
p. e67926

Abstract

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The identification of virulence genes in plant pathogenic fungi is important for understanding the infection process, host range and for developing control strategies. The analysis of already verified virulence genes in phytopathogenic fungi in the context of integrated functional networks can give clues about the underlying mechanisms and pathways directly or indirectly linked to fungal pathogenicity and can suggest new candidates for further experimental investigation, using a 'guilt by association' approach. Here we study 133 genes in the globally important Ascomycete fungus Fusarium graminearum that have been experimentally tested for their involvement in virulence. An integrated network that combines information from gene co-expression, predicted protein-protein interactions and sequence similarity was employed and, using 100 genes known to be required for virulence, we found a total of 215 new proteins potentially associated with virulence of which 29 are annotated as hypothetical proteins. The majority of these potential virulence genes are located in chromosomal regions known to have a low recombination frequency. We have also explored the taxonomic diversity of these candidates and found 25 sequences, which are likely to be fungal specific. We discuss the biological relevance of a few of the potentially novel virulence associated genes in detail. The analysis of already verified virulence genes in phytopathogenic fungi in the context of integrated functional networks can give clues about the underlying mechanisms and pathways directly or indirectly linked to fungal pathogenicity and can suggest new candidates for further experimental investigation, using a 'guilt by association' approach.