Fishing for Biomarkers; A Multivariate Approach to Scrutinize the Combined Metabolome-Transcriptome Profiles on Our Quest toward Biomarkers for Postharvest Apple Disorders

Tuesday, July 23, 2013
Desert Ballroom: Salons 7-8 (Desert Springs J.W Marriott Resort )
David R. Rudell , USDA–ARS, Tree Fruit Research Laboratory, Wenatchee, WA
Maarten Hertog , BIOSYST-MeBioS, Katholieke Universiteit Leuven, Heverlee, Belgium
Nigel Gapper , Cornell University, Ithaca, NY
Christopher B. Watkins , Department of Horticulture, Cornell University, Ithaca, NY
James Giovannoni , USDA–ARS, Boyce Thompson Institute, Ithaca, NY
James Mattheis , Tree Fruit Research Lab, USDA–ARS, Tree Fruit Research Laboratory, Wenatchee, WA
Jinwook Lee , Tree Fruit Research Lab, USDA–ARS, Tree Fruit Research Laboratory, Wenatchee, WA
Rachel Leisso , USDA–ARS, Tree Fruit Research Laboratory, Wenatchee, WA
Timely assessment of the risk for postharvest apple disorders, such as superficial scald, will help fruit producers better manage and optimize their postharvest revenues. As the industry currently is not able to predict if and to what extent apple fruit will develop postharvest disorders, there is an urgent need for biomarker-based tools that can. By screening changes at the various omic-levels preceeding, and in relation to the progressive disorder development, potential biomarkers can be identified. The main challenges are to find the proverbial needle in the haystack of candidates and to properly balance the contributions from the different sized omic-pools. We are applying a combined transcriptomics and metabolomics approach, monitoring over 30,000 genes and 600 metabolites as measured in apples stored under more or less stringent conditions triggering the disorders of interest to various extents. Multivariate analyses techniques have been applied to analyze the omic-datasets either in isolation, or together, to find the most relevant candidates. Using appropriate visualization techniques, changes in selected genes and metabolites were interpreted in their wider context. Starting by identifying genes and metabolites characteristic for the disorders studied, we moved toward differentiating among the effects of the various experimental factors. Beyond that, we focused on identifying markers that allow for segregation of treatments long before the disorders appear.
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