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Transcriptional Responses in Root and Leaf of Prunus persica under Drought Stress Using RNA Sequencing

View Article: PubMed Central - PubMed

ABSTRACT

Prunus persica L. Batsch, or peach, is one of the most important crops and it is widely established in irrigated arid and semi-arid regions. However, due to variations in the climate and the increased aridity, drought has become a major constraint, causing crop losses worldwide. The use of drought-tolerant rootstocks in modern fruit production appears to be a useful method of alleviating water deficit problems. However, the transcriptomic variation and the major molecular mechanisms that underlie the adaptation of drought-tolerant rootstocks to water shortage remain unclear. Hence, in this study, high-throughput sequencing (RNA-seq) was performed to assess the transcriptomic changes and the key genes involved in the response to drought in root tissues (GF677 rootstock) and leaf tissues (graft, var. Catherina) subjected to 16 days of drought stress. In total, 12 RNA libraries were constructed and sequenced. This generated a total of 315 M raw reads from both tissues, which allowed the assembly of 22,079 and 17,854 genes associated with the root and leaf tissues, respectively. Subsets of 500 differentially expressed genes (DEGs) in roots and 236 in leaves were identified and functionally annotated with 56 gene ontology (GO) terms and 99 metabolic pathways, which were mostly associated with aminobenzoate degradation and phenylpropanoid biosynthesis. The GO analysis highlighted the biological functions that were exclusive to the root tissue, such as “locomotion,” “hormone metabolic process,” and “detection of stimulus,” indicating the stress-buffering role of the GF677 rootstock. Furthermore, the complex regulatory network involved in the drought response was revealed, involving proteins that are associated with signaling transduction, transcription and hormone regulation, redox homeostasis, and frontline barriers. We identified two poorly characterized genes in P. persica: growth-regulating factor 5 (GRF5), which may be involved in cellular expansion, and AtHB12, which may be involved in root elongation. The reliability of the RNA-seq experiment was validated by analyzing the expression patterns of 34 DEGs potentially involved in drought tolerance using quantitative reverse transcription polymerase chain reaction. The transcriptomic resources generated in this study provide a broad characterization of the acclimation of P. persica to drought, shedding light on the major molecular responses to the most important environmental stressor.

No MeSH data available.


Quantitative reverse transcription polymerase chain reaction (RT-qPCR) validation of selected genes in roots (GF677 rootstock) and leaves (graft, var. Catherina) in the control and drought-stressed plants. The gray bars represent the relative expression determined by RT-qPCR (left y-axis) and the black bars represent the level of expression (RPKM) of the transcripts (right y-axis). The relative expression in the RT-qPCR analysis was normalized to the level in the GF677 rootstock of the control plants. The error bars indicate the standard error of quad-biological and bi-technical replicates. See abbreviations and further information about each gene in Supplementary Table S1.
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Figure 6: Quantitative reverse transcription polymerase chain reaction (RT-qPCR) validation of selected genes in roots (GF677 rootstock) and leaves (graft, var. Catherina) in the control and drought-stressed plants. The gray bars represent the relative expression determined by RT-qPCR (left y-axis) and the black bars represent the level of expression (RPKM) of the transcripts (right y-axis). The relative expression in the RT-qPCR analysis was normalized to the level in the GF677 rootstock of the control plants. The error bars indicate the standard error of quad-biological and bi-technical replicates. See abbreviations and further information about each gene in Supplementary Table S1.

Mentions: In order to further confirm the accuracy of the RNA-seq expression estimates, a total of 34 candidate genes were selected for RT-qPCR validation according to their RPKM transcript abundance and Log2FC. As illustrated in Figure 6, the expression values of the selected DEGs in both tissues significantly correlated with the RPKM values, with the exception of the chloroplastic ribulose bisphosphate carboxylase small chain (RBCS) gene, which may be a result of the unstable expression of this chloroplastic gene. The correlation between the RNA-seq and RT-qPCR measurements was evaluated using linear regression, based on the following equation: RT-qPCR value = b (RNA-Seq value) + a (Figure 7). Interestingly, the linear regression analysis indicated a highly significant correlation between the methods, indicating a general agreement regarding the transcript abundance determined by both methodologies (r = 0.89 and r = 0.95 for root and leaf DEGs, respectively). In conclusion, the obtained results confirm the reliability of the transcriptomic profiling data estimated from RNA-seq data.


Transcriptional Responses in Root and Leaf of Prunus persica under Drought Stress Using RNA Sequencing
Quantitative reverse transcription polymerase chain reaction (RT-qPCR) validation of selected genes in roots (GF677 rootstock) and leaves (graft, var. Catherina) in the control and drought-stressed plants. The gray bars represent the relative expression determined by RT-qPCR (left y-axis) and the black bars represent the level of expression (RPKM) of the transcripts (right y-axis). The relative expression in the RT-qPCR analysis was normalized to the level in the GF677 rootstock of the control plants. The error bars indicate the standard error of quad-biological and bi-technical replicates. See abbreviations and further information about each gene in Supplementary Table S1.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC5120087&req=5

Figure 6: Quantitative reverse transcription polymerase chain reaction (RT-qPCR) validation of selected genes in roots (GF677 rootstock) and leaves (graft, var. Catherina) in the control and drought-stressed plants. The gray bars represent the relative expression determined by RT-qPCR (left y-axis) and the black bars represent the level of expression (RPKM) of the transcripts (right y-axis). The relative expression in the RT-qPCR analysis was normalized to the level in the GF677 rootstock of the control plants. The error bars indicate the standard error of quad-biological and bi-technical replicates. See abbreviations and further information about each gene in Supplementary Table S1.
Mentions: In order to further confirm the accuracy of the RNA-seq expression estimates, a total of 34 candidate genes were selected for RT-qPCR validation according to their RPKM transcript abundance and Log2FC. As illustrated in Figure 6, the expression values of the selected DEGs in both tissues significantly correlated with the RPKM values, with the exception of the chloroplastic ribulose bisphosphate carboxylase small chain (RBCS) gene, which may be a result of the unstable expression of this chloroplastic gene. The correlation between the RNA-seq and RT-qPCR measurements was evaluated using linear regression, based on the following equation: RT-qPCR value = b (RNA-Seq value) + a (Figure 7). Interestingly, the linear regression analysis indicated a highly significant correlation between the methods, indicating a general agreement regarding the transcript abundance determined by both methodologies (r = 0.89 and r = 0.95 for root and leaf DEGs, respectively). In conclusion, the obtained results confirm the reliability of the transcriptomic profiling data estimated from RNA-seq data.

View Article: PubMed Central - PubMed

ABSTRACT

Prunus persica L. Batsch, or peach, is one of the most important crops and it is widely established in irrigated arid and semi-arid regions. However, due to variations in the climate and the increased aridity, drought has become a major constraint, causing crop losses worldwide. The use of drought-tolerant rootstocks in modern fruit production appears to be a useful method of alleviating water deficit problems. However, the transcriptomic variation and the major molecular mechanisms that underlie the adaptation of drought-tolerant rootstocks to water shortage remain unclear. Hence, in this study, high-throughput sequencing (RNA-seq) was performed to assess the transcriptomic changes and the key genes involved in the response to drought in root tissues (GF677 rootstock) and leaf tissues (graft, var. Catherina) subjected to 16 days of drought stress. In total, 12 RNA libraries were constructed and sequenced. This generated a total of 315 M raw reads from both tissues, which allowed the assembly of 22,079 and 17,854 genes associated with the root and leaf tissues, respectively. Subsets of 500 differentially expressed genes (DEGs) in roots and 236 in leaves were identified and functionally annotated with 56 gene ontology (GO) terms and 99 metabolic pathways, which were mostly associated with aminobenzoate degradation and phenylpropanoid biosynthesis. The GO analysis highlighted the biological functions that were exclusive to the root tissue, such as “locomotion,” “hormone metabolic process,” and “detection of stimulus,” indicating the stress-buffering role of the GF677 rootstock. Furthermore, the complex regulatory network involved in the drought response was revealed, involving proteins that are associated with signaling transduction, transcription and hormone regulation, redox homeostasis, and frontline barriers. We identified two poorly characterized genes in P. persica: growth-regulating factor 5 (GRF5), which may be involved in cellular expansion, and AtHB12, which may be involved in root elongation. The reliability of the RNA-seq experiment was validated by analyzing the expression patterns of 34 DEGs potentially involved in drought tolerance using quantitative reverse transcription polymerase chain reaction. The transcriptomic resources generated in this study provide a broad characterization of the acclimation of P. persica to drought, shedding light on the major molecular responses to the most important environmental stressor.

No MeSH data available.