Leveraging transcriptome data for enhanced gene expression analysis in apple

Authors: Hargarten, Heidi; WALIULLAH, SUMYYA - Washington State University; KALCSITS, LEE - Washington State University; Honaas, Loren

Submitted to: Journal of the American Society for Horticultural Science
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 5/17/2018
Publication Date: 9/1/2018
Citation: Hargarten, H.L., Waliullah, S., Kalcsits, L., Honaas, L.A. 2018. Leveraging transcriptome data for enhanced gene expression analysis in apple. Journal of the American Society for Horticultural Science. 143(5):333-346. https://doi.org/10.21273/JASHS04424-18.
DOI: https://doi.org/10.21273/JASHS04424-18

Interpretive Summary: Measuring the activity of genes is a fundamental tool for understanding biological processes. To do this, prior knowledge of the gene sequence is necessary to interpret the gene activity measurements. Any discrepancies in the prior knowledge, even minor differences in the gene, can cause gene activity measurement to be inaccurate, or even fail completely. In this work we show how publicly available data can be used to improve the accuracy of gene information. By improving the gene information, we show that gene activity measurements are more reliable and more accurate. This process was developed using apple fruit as an example, but is broadly applicable to other specialty crops and beyond. By increasing the accuracy and reliability of gene activity measurements, we enhance our knowledge of the biology they drive.

Technical Abstract: Complex changes in gene expression occur during postharvest storage of apple and often precede or accompany changes in ripening and disorder development. Targeted gene expression analysis fundamentally relies upon prior 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 accurate. 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 apple genome. We attempted to validate qPCR-based gene expression measurements with 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 organisms 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.