March
2010
, Volume
94
, Number
3
Pages
311
-
319
Authors
Jillian M. Lang, Department of Bioagricultural Sciences and Pest Management, Colorado State University (CSU), Fort Collins 80523-1177;
John P. Hamilton, Department of Plant Biology, Michigan State University, East Lansing 48824-1312;
Maria Genaleen Q. Diaz, Department of Bioagricultural Sciences and Pest Management, CSU and University of the Philippines, Los Baños, Los Baños, Philippines;
Marie Anne Van Sluys, Department of Bioagricultural Sciences and Pest Management, CSU and Departamento de Botânica, IB-USP, São Paulo 05508-090, Brazil;
Ma. Ruby G. Burgos and
Casiana M. Vera Cruz, Plant Breeding, Genetics and Biotechnology Division, International Rice Research Institute, DAPO Box 7777, Metro Manila, Philippines;
C. Robin Buell, Department of Plant Biology, Michigan State University; and
Ned A. Tisserat and
Jan E. Leach, Department of Bioagricultural Sciences and Pest Management, CSU
Affiliations
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RelatedArticle
Accepted for publication 21 October 2009.
Abstract
ABSTRACT
A computational genomics pipeline was used to compare sequenced genomes of Xanthomonas spp. and to rapidly identify unique regions for development of highly specific diagnostic markers. A suite of diagnostic primers was selected to monitor diverse loci and to distinguish the rice bacterial blight and bacterial leaf streak pathogens, Xanthomonas oryzae pv. oryzae and X. oryzae pv. oryzicola, respectively. A subset of these primers was combined into a multiplex polymerase chain reaction set that accurately distinguished the two rice pathogens in a survey of a geographically diverse collection of X. oryzae pv. oryzae, X. oryzae pv. oryzicola, other xanthomonads, and several genera of plant-pathogenic and plant- or seed-associated bacteria. This computational approach for identification of unique loci through whole-genome comparisons is a powerful tool that can be applied to other plant pathogens to expedite development of diagnostic primers.
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© 2010 The American Phytopathological Society