title: A comparative hidden Markov model analysis pipeline identifies proteins characteristic of cereal-infecting fungi.
abstract: BACKGROUND
  Fungal pathogens cause devastating losses in economically important cereal crops by utilising pathogen proteins to infect host plants. Secreted pathogen proteins are referred to as effectors and have thus far been identified by selecting small, cysteine-rich peptides from the secretome despite increasing evidence that not all effectors share these attributes.
  
  
  RESULTS
  We take advantage of the availability of sequenced fungal genomes and present an unbiased method for finding putative pathogen proteins and secreted effectors in a query genome via comparative hidden Markov model analyses followed by unsupervised protein clustering. Our method returns experimentally validated fungal effectors in Stagonospora nodorum and Fusarium oxysporum as well as the N-terminal Y/F/WxC-motif from the barley powdery mildew pathogen. Application to the cereal pathogen Fusarium graminearum reveals a secreted phosphorylcholine phosphatase that is characteristic of hemibiotrophic and necrotrophic cereal pathogens and shares an ancient selection process with bacterial plant pathogens. Three F. graminearum protein clusters are found with an enriched secretion signal. One of these putative effector clusters contains proteins that share a [SG]-P-C-[KR]-P sequence motif in the N-terminal and show features not commonly associated with fungal effectors. This motif is conserved in secreted pathogenic Fusarium proteins and a prime candidate for functional testing.
  
  
  CONCLUSIONS
  Our pipeline has successfully uncovered conservation patterns, putative effectors and motifs of fungal pathogens that would have been overlooked by existing approaches that identify effectors as small, secreted, cysteine-rich peptides. It can be applied to any pathogenic proteome data, such as microbial pathogen data of plants and other organisms.
authors.affiliation: Commonwealth Scientific and Industrial Research Organization (CSIRO) Plant Industry, Centre for Environment and Life Sciences, Perth, Western Australia, Australia. jana.sperschneider@csiro.au.
authors.forename: Jana
authors.initials: J
authors.lastname: Sperschneider
authors.forename: Donald M
authors.initials: DM
authors.lastname: Gardiner
authors.forename: Jennifer M
authors.initials: JM
authors.lastname: Taylor
authors.forename: James K
authors.initials: JK
authors.lastname: Hane
authors.forename: Karam B
authors.initials: KB
authors.lastname: Singh
authors.forename: John M
authors.initials: JM
authors.lastname: Manners
mesh_terms: D000595:Amino Acid Sequence; D016000:Cluster Analysis; D002523:Edible Grain / Q000382:microbiology*; D005656:Fungal Proteins / Q000235:genetics*; D005670:Fusarium / Q000235:genetics / Q000472:pathogenicity*; D016681:Genome, Fungal; D008390:Markov Chains; D015233:Models, Statistical; D008969:Molecular Sequence Data; D010935:Plant Diseases / Q000382:microbiology*; D020543:Proteome / Q000235:genetics; D014774:Virulence / Q000235:genetics
chemical_list: D005656:Fungal Proteins; D020543:Proteome