Sequence mining and transcript profiling to explore cyst nematode parasitism

dc.contributor.author Baum, Thomas
dc.contributor.author Mitreva, Makedonka
dc.contributor.author Nettleton, Dan
dc.contributor.author Martin, John
dc.contributor.author Recknor, Justin
dc.contributor.author Davis, Eric
dc.contributor.author Hussey, Richard
dc.contributor.author Nettleton, Dan
dc.contributor.author McCarter, James
dc.contributor.author Baum, Thomas
dc.contributor.department Plant Pathology and Microbiology
dc.contributor.department Statistics
dc.contributor.department Genetics
dc.date 2018-02-18T18:44:09.000
dc.date.accessioned 2020-06-30T06:22:18Z
dc.date.available 2020-06-30T06:22:18Z
dc.date.copyright Thu Jan 01 00:00:00 UTC 2009
dc.date.issued 2009-01-01
dc.description.abstract <p>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.</p> <p>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.</p> <p>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.</p>
dc.description.comments <p>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." <em>Bmc Genomics</em> 10, no. 1 (2009): 58, doi: <a href="https://doi.org/10.1186/1471-2164-10-58" target="_blank">10.1186/1471-2164-10-58</a>. Posted with permission.</p>
dc.format.mimetype application/pdf
dc.identifier archive/lib.dr.iastate.edu/plantpath_pubs/133/
dc.identifier.articleid 1151
dc.identifier.contextkey 10533167
dc.identifier.s3bucket isulib-bepress-aws-west
dc.identifier.submissionpath plantpath_pubs/133
dc.identifier.uri https://dr.lib.iastate.edu/handle/20.500.12876/57576
dc.language.iso en
dc.source.bitstream archive/lib.dr.iastate.edu/plantpath_pubs/133/2009_Baum_SequenceMining.pdf|||Fri Jan 14 19:49:27 UTC 2022
dc.source.uri 10.1186/1471-2164-10-58
dc.subject.disciplines Agricultural Science
dc.subject.disciplines Agriculture
dc.subject.disciplines Genetics and Genomics
dc.subject.disciplines Plant Pathology
dc.subject.disciplines Statistical Methodology
dc.title Sequence mining and transcript profiling to explore cyst nematode parasitism
dc.type article
dc.type.genre article
dspace.entity.type Publication
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