bioRxivPlant BiologyPreprint
Decoding strawberry volatile cultivar diversity through comparative transcriptome analysis
K. Passa, E. Tsormpatsidis, I. Ganopoulos, C. Bazakos, V. Papasotiropoulos
Abstract
This study presents a comparative transcriptomic analysis of three commercial strawberry cultivars: Rociera, Calderon, and Victory, aimed at uncovering the molecular basis of their distinct flavor and aroma profiles. Through RNA sequencing, we analyzed the transcriptomic landscape of these varieties, uncovering a notable array of differentially expressed genes (DEGs) between them. Specifically, Rociera showed a higher count of DEGs compared to Victory, suggesting significant differences in gene expression related to flavor and aroma between those cultivars. Our further investigations revealed pivotal metabolic pathways-- such as those involving furanones, amino acids, fatty acids, and terpenoids--participating in generating volatile organic compounds in strawberries. These pathways exhibited variety-specific expression patterns, underlining the genetic determinants of each varietys unique sensory characteristics. Gene Ontology (GO) enrichment analysis underscored important pathways like phototropism, sugar-mediated signaling, and terpenoid biosynthesis, highlighting the genetic intricacy that influences strawberry flavor and aroma. Additionally, our study identified 31,677 single nucleotide polymorphisms (SNPs) with a significant impact on genes associated with volatile compound biosynthesis. Notably, SNPs linked to key enzymes such as lipoxygenase 6, omega-6 fatty acid desaturase (FaFAD1), and phenylalanine ammonia-lyase 1 (PAL) were discovered, shedding light on the genetic variations that underlie flavor differences. Therefore, this research advances our comprehension of the genetic elements that impact strawberry fruit flavor and aroma, offering invaluable insights for molecular breeding endeavors focused on enhancing strawberry flavor.
- organism: plant (other)
- assay: bulk RNA-seq
- research focus: gene regulatory network inference
- approach: statistical model
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