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ARS Home » Pacific West Area » Wenatchee, Washington » Physiology and Pathology of Tree Fruits Research » Research » Research Project #442365

Research Project: Towards Next Generation Maturity Indices: Apple Biomarker Discovery

Location: Physiology and Pathology of Tree Fruits Research

Project Number: 2094-43000-008-036-T
Project Type: Trust Fund Cooperative Agreement

Start Date: Apr 1, 2022
End Date: Mar 31, 2025

Objective: Develop and improve methods for biomarker discovery: a. Use novel analytics and modeling approaches to strengthen biomarker discovery approach. b. Generate new global-scale gene activity data from current and new multi-year samples for rapid validation. c. Investigate disagreement between technologies for gene activity estimates to enhance translation to Next Generation Maturity Indexes (NGMIs).

We propose to bring in-hand data, which includes 4 cultivar/years of harvest time course gene activity data (hundreds of RNA Seq libraries) and new data from this proposed work (hundreds of validation samples from 30 cultivar/years) together with new apple genomes (3 from the Honaas lab and other published apple genomes). This will produce a high definition look at apple gene expression that captures the developmental transition from "immature" to "mature," which essentially means that fruit have gained the ability to ripen. The approach for Objective A is to explore cutting edge modeling approaches to discover gene activity signatures that are relatable to changes in fruit maturity. This includes Honaas' published methods, and other published work, plus novel approaches that are in development in the PIs labs. For Objective B, we will sequence our multi-year, multi-orchard, multi-cultivar validation set (that we will continue to grow in this project). This will produce a data matrix of all gene activity in all samples, allowing our models (developed using in-hand RNA Seq data) to be rapidly evaluated. As soon as a prediction is made, it can be immediately tested across 30+ cultivar/years. For Objective C, we will continue to examine and compare gene expression methods towards improving the transferability of information, specifically from digital gene expression (RNA Seq) to targeted assays (qPCR). Honaas' and other's published work shows that roughly 1 in 4 gene activity signatures is not reliably reproduced in targeted assays. This presents a hurdle to the development of biomarkers. We aim to examine how various methods, genotypes, genome quality, and individual sequence characteristics influence the agreement, and thus transferability, between RNA seq and qPCR.