Katja Jamnik*, Hana Šinkovec, Jernej Jakše, Vanja Miljanić and Nataša Štajner
Biotechnical faculty, University of Ljubljana, Slovenia
katja.jamnik [at] bf.uni-lj.si
Abstract
Grapevine (Vitis vinifera L.) is one of the most important fruit crops worldwide. Its production is threatened by numerous pathogens, including viruses, which can cause significant economic losses. Plants respond to viral infection through multiple regulatory mechanisms, including changes in microRNA (miRNA) expression. miRNAs are a class of small non-coding RNAs that regulate gene expression at the post-transcriptional level. In this study, we compared miRNA predictions generated by three widely used miRNA prediction tools in grapevine under viral coinfection.
Virus-free and virus-infected plants of two grapevine cultivars, Refošk (“Terrano”) and Zeleni Sauvignon (“Sauvignon Vert”), infected with Grapevine Pinot gris virus (GPGV), Grapevine rupestris stem pitting-associated virus (GRSPaV), and Grapevine rupestris vein feathering virus (GRVFV), were analyzed. Small RNA sequencing was performed, and three prediction tools (miRador, miRDeep2, and ShortStack) were used to identify and quantify miRNAs. Differential expression analysis was conducted separately for each tool and using an integrated approach combining all three datasets. The expression of selected miRNAs was further validated by stem-loop RT-qPCR.
By small RNA sequencing, between 6,344,090 and 25,041,733 reads per library were obtained. The presence of GPGV, GRSPaV, and GRVFV in infected samples and absence of viruses in the virus-free plants were confirmed through sRNA sequencing data. Each prediction tool identified a distinct number of miRNAs, with miRDeep2 detecting the highest number and miRador the lowest. Consequently, the sets of differentially expressed miRNAs varied substantially between tools. The integrated approach yielded an additional set of differentially expressed miRNAs, most of which overlapped with those identified by at least one individual tool. Stem-loop RT-qPCR supported the differential expression of several selected miRNAs.
Overall, our results demonstrate that miRNA profiling outcomes in grapevine under viral coinfection are strongly dependent on the choice of prediction tool. This highlights the need for integrative analytical approaches combined with experimental validation to achieve reliable and biologically meaningful interpretation of miRNA expression data.
Keywords: miRNA, prediction tools, differential expression
Acknowledgement: This research was funded by Slovenian Resarch and Innovation Agency (ARIS), grant number P4-0077 and ARIS grant to young researcher Katja Jamnik.