title: The prediction of a pathogenesis-related secretome of Puccinia helianthi through high-throughput transcriptome analysis.
abstract: BACKGROUND
  Many plant pathogen secretory proteins are known to be elicitors or pathogenic factors,which play an important role in the host-pathogen interaction process. Bioinformatics approaches make possible the large scale prediction and analysis of secretory proteins from the Puccinia helianthi transcriptome. The internet-based software SignalP v4.1, TargetP v1.01, Big-PI predictor, TMHMM v2.0 and ProtComp v9.0 were utilized to predict the signal peptides and the signal peptide-dependent secreted proteins among the 35,286 ORFs of the P. helianthi transcriptome.
  
  
  RESULTS
  908 ORFs (accounting for 2.6% of the total proteins) were identified as putative secretory proteins containing signal peptides. The length of the majority of proteins ranged from 51 to 300 amino acids (aa), while the signal peptides were from 18 to 20 aa long. Signal peptidase I (SpI) cleavage sites were found in 463 of these putative secretory signal peptides. 55 proteins contained the lipoprotein signal peptide recognition site of signal peptidase II (SpII). Out of 908 secretory proteins, 581 (63.8%) have functions related to signal recognition and transduction, metabolism, transport and catabolism. Additionally, 143 putative secretory proteins were categorized into 27 functional groups based on Gene Ontology terms, including 14 groups in biological process, seven in cellular component, and six in molecular function. Gene ontology analysis of the secretory proteins revealed an enrichment of hydrolase activity. Pathway associations were established for 82 (9.0%) secretory proteins. A number of cell wall degrading enzymes and three homologous proteins specific to Phytophthora sojae effectors were also identified, which may be involved in the pathogenicity of the sunflower rust pathogen.
  
  
  CONCLUSIONS
  This investigation proposes a new approach for identifying elicitors and pathogenic factors. The eventual identification and characterization of 908 extracellularly secreted proteins will advance our understanding of the molecular mechanisms of interactions between sunflower and rust pathogen and will enhance our ability to intervene in disease states.
authors.affiliation: Department of Plant Pathology, Inner Mongolia Agricultural University, Hohhot, 010019, China. jinglan71@126.com.
authors.forename: Lan
authors.initials: L
authors.lastname: Jing
authors.affiliation: Department of Plant Pathology, Inner Mongolia Agricultural University, Hohhot, 010019, China.
authors.forename: Dandan
authors.initials: D
authors.lastname: Guo
authors.affiliation: Department of Plant Pathology, Inner Mongolia Agricultural University, Hohhot, 010019, China.
authors.forename: Wenjie
authors.initials: W
authors.lastname: Hu
authors.affiliation: Division of Plant Sciences, University of Missouri, Columbia, MO, 65211, USA.
authors.forename: Xiaofan
authors.initials: X
authors.lastname: Niu
mesh_terms: D000465:Algorithms; D016282:Aspartic Acid Endopeptidases / Q000378:metabolism; D001426:Bacterial Proteins / Q000378:metabolism; D001487:Basidiomycota / Q000378:metabolism* / Q000472:pathogenicity; D005656:Fungal Proteins / Q000378:metabolism; D020869:Gene Expression Profiling; D006368:Helianthus / Q000382:microbiology; D016366:Open Reading Frames; D010935:Plant Diseases / Q000382:microbiology; D018515:Plant Leaves / Q000382:microbiology; D021382:Protein Sorting Signals; D059467:Transcriptome
chemical_list: D001426:Bacterial Proteins; D005656:Fungal Proteins; D021382:Protein Sorting Signals; D016282:Aspartic Acid Endopeptidases; C095652:signal peptidase II
keywords: Bioinformatics; Prediction algorithm; Puccinia helianthi Schw.; Secretory protein; Signal peptide