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Functional Genomics and Bioinformatics of the Phytophthora sojae Soybean Interaction

  • Brett M. Tyler
  • Rays H.Y. Jiang
  • Lecong Zhou
  • Sucheta Tripathy
  • Daolong Dou
  • Trudy Torto-Alalibo
  • Hua Li
  • Yongcai Mao
  • Bing Liu
  • Miguel Vega-Sanchez
  • Santiago X. Mideros
  • Regina Hanlon
  • Brian M. Smith
  • Konstantinos Krampis
  • Keying Ye
  • Steven St. Martin
  • Anne E. Dorrance
  • Ina Hoeschele
  • M.A. Saghai Maroof
Part of the Stadler Genetics Symposia Series book series (SGSS)

Abstract

Oomycete plant pathogens such as Phytophthora species and downy mildews cause destructive diseases in an enormous variety of crop plant species as well as forests and native ecosystems. These pathogens are most closely related to algae in the kingdom Stramenopiles, and hence have evolved plant pathogenicity independently of other plant pathogens such as fungi. We have used bioinformatic analysis of genome sequences and EST collections, together with functional genomics to identify plant and pathogen genes that may be key players in the interaction between the soybean pathogen Phytophthora sojae and its host. In P. sojae, we have identified many rapidly diversifying gene families that encode potential pathogenicity factors including protein toxins, and a class of proteins (avirulence or effector proteins) that appear to have the ability to penetrate plant cells. Transcriptomic analysis of quantitative or multigenic resistance against P. sojae in soybean has revealed that there are widespread adjustments in host gene expression in response to infection, and that some responses are unique to particular resistant cultivars. These observations lay the foundation for dissecting the interplay between pathogen and host genes during infection at a whole-genome level.

Keywords

Late Blight Downy Mildew Quantitative Resistance Phytophthora Species Avirulence Gene 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media, LLC 2008

Authors and Affiliations

  • Brett M. Tyler
    • 1
  • Rays H.Y. Jiang
  • Lecong Zhou
  • Sucheta Tripathy
  • Daolong Dou
  • Trudy Torto-Alalibo
  • Hua Li
  • Yongcai Mao
  • Bing Liu
  • Miguel Vega-Sanchez
  • Santiago X. Mideros
  • Regina Hanlon
  • Brian M. Smith
  • Konstantinos Krampis
  • Keying Ye
  • Steven St. Martin
  • Anne E. Dorrance
  • Ina Hoeschele
  • M.A. Saghai Maroof
  1. 1.Virginia Bioinformatics Institute, Virginia Polytechnic Institute and State UniversityBlacksburgUSA

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