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Sequence mining and transcript profiling to explore cyst nematode parasitism
BMC Genomics
  • Axel A Elling, Iowa State University
  • Makedonka Mitreva, Washington University School of Medicine in St. Louis
  • Xiaowu Gai, The Children's Hospital of Philadelphia
  • John Martin, Washington University School of Medicine in St. Louis
  • Justin Recknor, Iowa State University
  • Eric L Davis, North Carolina State University
  • Richard S Hussey, University of Georgia
  • Dan Nettleton, Iowa State University
  • James P McCarter, Washington University School of Medicine in St. Louis
  • Thomas J Baum, Iowa State University
Document Type
Article
Publication Version
Published Version
Publication Date
1-1-2009
DOI
10.1186/1471-2164-10-58
Abstract
Background: Cyst nematodes are devastating plant parasites that become sedentary within plant roots and induce the transformation of normal plant cells into elaborate feeding cells with the help of secreted effectors, the parasitism proteins. These proteins are the translation products of parasitism genes and are secreted molecular tools that allow cyst nematodes to infect plants. Results: We present here the expression patterns of all previously described parasitism genes of the soybean cyst nematode, Heterodera glycines, in all major life stages except the adult male. These insights were gained by analyzing our gene expression dataset from experiments using the Affymetrix Soybean Genome Array GeneChip, which contains probeset sequences for 6,860 genes derived from preparasitic and parasitic H. glycines life stages. Targeting the identification of additional H. glycines parasitism-associated genes, we isolated 633 genes encoding secretory proteins using algorithms to predict secretory signal peptides. Furthermore, because some of the known H. glycines parasitism proteins have strongest similarity to proteins of plants and microbes, we searched for predicted protein sequences that showed their highest similarities to plant or microbial proteins and identified 156 H. glycines genes, some of which also contained a signal peptide. Analyses of the expression profiles of these genes allowed the formulation of hypotheses about potential roles in parasitism. This is the first study combining sequence analyses of a substantial EST dataset with microarray expression data of all major life stages (except adult males) for the identification and characterization of putative parasitism-associated proteins in any parasitic nematode. Conclusion: We have established an expression atlas for all known H. glycines parasitism genes. Furthermore, in an effort to identify additional H. glycines genes with putative functions in parasitism, we have reduced the currently known 6,860 H. glycines genes to a pool of 788 most promising candidate genes (including known parasitism genes) and documented their expression profiles. Using our approach to pre-select genes likely involved in parasitism now allows detailed functional analyses in a manner not feasible for larger numbers of genes. The generation of the candidate pool described here is an important enabling advance because it will significantly facilitate the unraveling of fascinating plant-animal interactions and deliver knowledge that can be transferred to other pathogen-host systems. Ultimately, the exploration of true parasitism genes verified from the gene pool delineated here will identify weaknesses in the nematode life cycle that can be exploited by novel anti-nematode efforts.
Comments

This article is published as Elling, Axel A., Makedonka Mitreva, Xiaowu Gai, John Martin, Justin Recknor, Eric L. Davis, Richard S. Hussey, Dan Nettleton, James P. McCarter, and Thomas J. Baum. "Sequence mining and transcript profiling to explore cyst nematode parasitism." Bmc Genomics 10, no. 1 (2009): 58, doi: 10.1186/1471-2164-10-58. Posted with permission.

Creative Commons License
Creative Commons Attribution 4.0
Copyright Owner
Elling et al
Language
en
File Format
application/pdf
Citation Information
Axel A Elling, Makedonka Mitreva, Xiaowu Gai, John Martin, et al.. "Sequence mining and transcript profiling to explore cyst nematode parasitism" BMC Genomics Vol. 10 (2009) p. 58
Available at: http://works.bepress.com/thomas-baum/30/