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United States Department of Agriculture

Agricultural Research Service

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Research Project: A Diagnostic Toolbox for Integrated Management of Apple Postharvest Necrotic Disorders (Plant and Food Contribution)

Location: Physiology and Pathology of Tree Fruits Research

2011 Annual Report

1a. Objectives (from AD-416)
1. Discover and validate diagnostic biomarkers that predict, diagnose, and/or distinguish apple postharvest physiological disorders. 2. Compile sets of biomarkers that could be used to predict, diagnose, or distinguish apple postharvest browning disorders and test their efficacy by classifying/reclassifying browning disorders based on new metabolic/genetic information. 3. Actively facilitate transfer of new biomarker-based technology for immediate implementation using current platforms and development of new tailored platforms utilizing biomarker-based technology.

1b. Approach (from AD-416)
Conduct an advisory panel, industry, and academic query that will assess current needs, scope, and understanding of diagnosis and non-chemical management of postharvest browning disorders, representing a variety of similar disorders occurring in multiple ecomically important cultivars, have been selected for this study. Employ experimental strategies that ulilize susceptable cultivars along with chemical and cultural controls that both control the selected disorders while accentuating metabolic differences and differences in gene expression between healthy apples and disorder-prone apples. Comprehensive metabolic and gene-expression profiling will be employed to discover disorder-specific diagostic biochemical and genetic biomarkers. Metabolic and gene-expession evaluations will be screened by the bioinformatics cooperators. Metabolic and gene expression profiles will be statistically modeled and mined by statistics/modeling cooperator to determine disorder-related metabolic changes and select biomarkers that best predict whether an apple will develop a certain disorder of differential that distinguish from others. Biomarkers associated with individual disorder will be compiled and compared to select those that will be the bases of discrimination of postharvest browning disorders. An economic study will validate cost-effectiveness biomarker-based management strategies and platforms. New tools will be used to classify or re-classify disorders using the new metabolic information. Diagnostic/prediction biomarkers and tools will be presented to fruit producers, retailers, and agricultural service companies in extension, industry, and scientific meeting to determine the best means for pilot testing and implementation of this new storage management and quality assurance technology.

3. Progress Report
This project relates to objective 1 of the associated in-house project which seeks to identify factors that influence postharvest fruit quality and development of market limiting physiological disorders. Postharvest physiological disorders of apple fruit cause significant annual postharvest losses to susceptible cultivars. Understanding of the genetic and metabolic causes of these disorders and similarities remains sparse. Current treatment practices are not available for organic production, are not acceptable in many markets, or do not provide quality assurance along the supply chain. Biomarker-based storage management tools are expected to provide the US apple industry with additional tools to prevent and predict losses throughout the supply chain. Collaborators at The New Zealand Institute for Plant and Food Research Limited are also experts at using postharvest apple storage techniques coupled with transcriptomic profiling to discover biomarkers related to apple postharvest storage disorders. They are expected to provide second-site validation of predictive and diagnostic biomarkers for apple postharvest disorders. Collaborator progress towards outlined milestones will be monitored by ARS using audio and video conferencing, site visits for collaboration, yearly reporting among team members, and publication.

4. Accomplishments

Last Modified: 10/18/2017
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