Leveraging Transcriptome Data for Enhanced Gene Expression Analysis in Apple

Abstract

Complex changes in gene expression occur during postharvest storage of apple (Malus ×domestica) and often precede or accompany changes in ripening and disorder development. Targeted gene expression analysis fundamentally relies on previous knowledge of the targeted gene. Minimally, a substantial fragment of the gene sequence must be known with high accuracy so that primers and probes, which bind to their targets in a complimentary fashion, are highly specific. Here, we describe a workflow that leverages publicly available transcriptome data to discover apple cultivar–specific gene sequences to guide primer design for quantitative real-time polymerase chain reaction (qPCR). We find that problematic polymorphisms occur frequently in ‘Granny Smith’ and ‘Honeycrisp’ apple when candidate primer binding sites were selected using the ‘Golden Delicious’ genome. We attempted to validate qPCR-based gene expression measurements with RNA sequencing (RNA-Seq) analysis of the same RNA samples. However, we found that agreement between the two technologies was highly variable and positively correlated with the similarity between cultivar-specific genes and RNA-Seq reference genes. Thus, we offer insight that 1) improves the accuracy and efficiency of qPCR primer design in cultivars that lack sufficient sequence resources and 2) better guides the essential step of validation of RNA-Seq data with a subset of genes of interest examined via qPCR.