Global Transcriptome Analysis Reveals Differences in Gene Expression Patterns Between Nonhyperhydric and Hyperhydric Peach Leaves

Abstract

Hyperhydricity is a morphophysiological disorder of plants in tissue culture characterized morphologically by the presence of translucent, thick, curled, and fragile leaves as a result of excessive water intake. Since clonal propagation is a major in vitro technique for multiplying plants vegetatively, the emergence of hyperhydricity-related symptoms causes significant economic losses to agriculture and horticulture. Although numerous efforts have been hitherto devoted to the morphological and anatomical responses of plants to hyperhydricity, the underlying molecular mechanism remains largely unknown. Here, a genome-wide transcriptome analysis was performed to identify differentially expressed genes in hyperhydric and nonhyperhydric leaves of peach [Prunus persica (L.) Batsch]. The RNA sequencing (RNASeq) analysis showed that the expression of >300 transcripts was altered between control and hyperhydric leaf cells. The top 30 differentially expressed transcripts (DETs) were related to the posttranscriptional regulators of organelle gene expression and photosynthesis, cellular elimination, plant cuticle development, and abiotic stress response processes. The expression of 10 DETs was also conformed by quantitative real-time polymerase chain reaction (RT-qPCR) in hyperhydric and nonhyperhydric leaves. As a complex biological process, hyperhydricity alters the expression of various transcripts including transcription factor (Myb2), RNA binding protein (pentatricopeptide, PPR), transporter protein (ABC), and Laccase3. Thus, this genome-wide transcriptome profiling study may help elucidate the molecular mechanism of hyperhydricity.

Potential of Transcript Editing Across Mitogenomes of Early Land Plants Shows Novel and Familiar Trends

“…Hence, further analyses of RNA editing in liverworts and mosses were restricted mainly to C-to-U editing sites and it should be noted that some discrepancy between the obtained dataset and database-derived transcriptomes is to be expected, not only due to the use of different individuals for RNA editing site predictions and transcriptome analyses. Recent studies have shown that RNA editing patterns and level differences may be caused by stress factors [51,52,53,54,55], diseases [56,57], as well as different composition of cell populations [58] and tissues [59]. Considering the aforementioned issues, some similarity in RNA editing site distribution between genome-based predictions and database-derived transcripts was expected.…”

Comparative transcriptome analysis of flower heterosis in two soybean F1 hybrids by RNA-seq

“…When the funding is not enough, the transcriptome analysis also was performed without biological relicates. But the gene expression profiles of key genes must be verified using qRT-PCR [42]. In our studies, 20 key genes were selected to verified the results of RNA-seq with qRT-PCR.…”

Barley Long Non-Coding RNAs and Their Tissue-Specific Co-expression Pattern with Coding-Transcripts

“…In the eTM sequences analysis pipeline, we utilized mature miRNA sequences of barley collected from miRBase (release 21) (Griffiths-Jones, 2006). To identify potential target transcripts of barley miRNAs, we utilized psRNATarget, an online miRNA target analysis tool (Dai and Zhao, 2011) as previously described previously (Akdogan et al, 2016; Bakir et al, 2016; Eldem et al, 2012; Inal et al, 2014; Yanik et al, 2013).…”