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Title:   Author  item
                                            Wise, Rogeritem
                                            MOSCOU, M - IOWA STATE UNIVERSITYitem
                                            WHITHAM, S - IOWA STATE UNIVERSITYitem
                                            BOGDANOVE, A - IOWA STATE UNIVERSITY
  Submitted to: Annual Review of Phytopathology
                                    Publication Type: Review Article
                                    Publication Acceptance Date: 4/27/2007
                                    Publication  Date: 9/3/2007
                                    Citation: Wise, R.P., Moscou, M.M., Whitham, S.A., Bogdanove, A.J. 2007. Transcript Profiling in Host-pathogen Interactions. Annual Review of Phytopathology. 45:329-369.Interpretive Summary:
                                Technical Abstract: It is now possible to address research hypotheses in the context of entire developmental or biochemical pathways, gene networks, chromosomal location of relevant genes and their inferred evolutionary history, and a large array of experimental or field conditions.  Access to complete genomic sequences, coupled with rapidly accumulating data related to RNA and protein expression patterns, has made it possible to determine comprehensively how genes contribute to complex phenotypes.  Parallel expression profiling coupled to clever experimental design has facilitated a system-wide approach to unanswered questions and unifying themes in the interactions among hosts and pathogens.  Interesting questions for plant pathologists might be, for example, what are the mechanisms underlying gene-for-gene resistance and basal defense, host vs. non-host resistance, biotrophy vs. necrotrophy, or pathogenicity of vascular vs. non-vascular pathogens, among many others.  Therefore, our approach in this review will not be to catalogue all the technologies or all the experiments that have been done, but rather to focus on key systems as examples of how parallel transcript profiling has been utilized effectively to further drive biological discovery in host-pathogen interactions.


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