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/re-classifying browning disorders based on new metabolic/genetic information.
3. Estimate the economic impact (both benefits and costs) to the apple industry of utilizing biomarker-based diagnostic tools to manage apple postharvest physiological disorders.
4. Actively facilitate transfer of new biomarker-based technology for immediate implementation using currents 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 economically important cultivars, have been selected for this study. Employ experimental strategies that utilize susceptible 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 disorder from others. Biomarkers associated with individual disorder will be compiled and compared to select those that will be the basis 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.
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 KU Leuven are experts in statistical evaluation and modeling of data from postharvest research and are expected to aid in visualizing vast amounts of metabolomic and transcriptomic data gathered by other team members and developing statistical techniques that screen and select biomarkers best suited for predicting and or diagnosing 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.