Leveraging Transcriptome Data for Enhanced Gene Expression Analysis in Apple

Abstract

<jats:p>Complex changes in gene expression occur during postharvest storage of apple (<jats:italic>Malus</jats:italic> ×<jats:italic>domestica</jats:italic>) 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.</jats:p>

Towards identification of postharvest fruit quality transcriptomic markers in Malus domestica

“…Candidate genes and their primer characteristics can be found in S5 Table in S4 File (CDS and primer alignments in S2 File ). Reference genes previously used in [ 56 ] and [ 4 ] were selected from the literature: MDP0000274900 [ 57 ], MDP0000173025 [ 58 ], and MDP0000223691 [ 59 ]. The GDDH13 homologous sequences for these reference genes were identified, using PlantTribes2, as MD09G1190100, MD16G1209000, and MD15G1211100 respectively [ 60 ].…”

“…W4502; Sigma-Aldrich, St. Louis, MO) to produce 100 μm solutions, and stored at –20°C. qPCR was performed as described in [ 56 ] for ‘Granny Smith’ on a subset of 33 Year 2 samples (corresponding to similar early and late postharvest storage time points from each experimental treatment and condition in Year 1—S6 Table in S4 File ) with a slight modification to the protocol: the qPCR reaction volume was increased to 15 μL [to accommodate automated liquid handling by an epMotion 5073 (Eppendorf, Hamburg, Germany)] by increasing the volume of SYBR per reaction while maintaining the template mass per reaction (10 pg cDNA).…”

Transcriptomics of Differential Ripening in ‘d’Anjou’ Pear (Pyrus communis L.)

“…A principle components analysis ( Supplementary Figure 1 ) of our pilot GEM (peel data) revealed that the transcriptome data had sufficient structure to distinguish fruit based on canopy position and tissue, thus providing contrasts to explore molecular signatures of differential ripening between canopy positions, as well as search for possible biosignatures. We selected genes for qPCR validation following Hargarten et al (2018) and the validation showed an average R 2 of 0.72 for the linear regression of all independent biological replicates ( R 2 = 0.83 for means) across a subset of samples for the each of the 10 candidate genes ( Supplementary Table 1 ).…”

“…RNA-seq estimates of gene activity were then validated on a subset of genes via qPCR. The subset of genes selected was based on the workflow described in Hargarten et al (2018) . The following criteria were used to select genes: (1) cumulative reads per kilobase transcript per million reads (RPKM) ≥ 2,000 ( n = 1,830 genes), (2) standard deviation between 50–100 RPKMs ( n = 47), (3) linear relationship with time in a subset of samples ( n = 19), (4) high identity matches between de novo ‘d’Anjou’ transcripts and Bartlett genome reference.…”

“…The 10 sequences with the highest overall similarity to Bartlett v1.0 reference sequences ( Supplementary Table 1 ) were selected for qPCR validation. qPCR was performed as described in Hargarten et al (2018) on the subset of RNA samples listed in Supplementary Table 2 using primers listed in Supplementary Table 3 .…”