Dataset Information

Unique expression, processing regulation, and regulatory network of peach (Prunus persica) miRNAs.

ABSTRACT: BACKGROUND: MicroRNAs (miRNAs) have recently emerged as important gene regulators in plants. MiRNAs and their targets have been extensively studied in Arabidopsis and rice. However, relatively little is known about the characterization of miRNAs and their target genes in peach (Prunus persica), which is a complex crop with unique developmental programs. RESULTS: We performed small RNA deep sequencing and identified 47 peach-specific and 47 known miRNAs or families with distinct expression patterns. Together, the identified miRNAs targeted 80 genes, many of which have not been reported previously. Like the model plant systems, peach has two of the three conserved trans-acting siRNA biogenesis pathways with similar mechanistic features and target specificity. Unique to peach, three of the miRNAs collectively target 49 MYBs, 19 of which are known to regulate phenylpropanoid metabolism, a key pathway associated with stone hardening and fruit color development, highlighting a critical role of miRNAs in the regulation of peach fruit development and ripening. We also found that the majority of the miRNAs were differentially regulated in different tissues, in part due to differential processing of miRNA precursors. Up to 16% of the peach-specific miRNAs were differentially processed from their precursors in a tissue specific fashion, which has been rarely observed in plant cells. The miRNA precursor processing activity appeared not to be coupled with its transcriptional activity but rather acted independently in peach. CONCLUSIONS: Collectively, the data characterizes the unique expression pattern and processing regulation of peach miRNAs and demonstrates the presence of a complex, multi-level miRNA regulatory network capable of targeting a wide variety of biological functions, including phenylpropanoid pathways which play a multifaceted spatial-temporal role in peach fruit development.

SUBMITTER: Zhu H

PROVIDER: S-EPMC3542160 | biostudies-literature | 2012

REPOSITORIES: biostudies-literature

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Unique expression, processing regulation, and regulatory network of peach (Prunus persica) miRNAs.

Zhu Hong H Xia Rui R Zhao Bingyu B An Yong-qiang YQ Dardick Chris D CD Callahan Ann M AM Liu Zongrang Z

BMC plant biology 20120821

<h4>Background</h4>MicroRNAs (miRNAs) have recently emerged as important gene regulators in plants. MiRNAs and their targets have been extensively studied in Arabidopsis and rice. However, relatively little is known about the characterization of miRNAs and their target genes in peach (Prunus persica), which is a complex crop with unique developmental programs.<h4>Results</h4>We performed small RNA deep sequencing and identified 47 peach-specific and 47 known miRNAs or families with distinct expr ...[more]

PMID: 22909020

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Project description:Genotype-by-environment interaction (G × E) is a common phenomenon influencing genetic improvement in plants, and a good understanding of this phenomenon is important for breeding and cultivar deployment strategies. However, there is little information on G × E in horticultural tree crops, mostly due to evaluation costs, leading to a focus on the development and deployment of locally adapted germplasm. Using sweetness (measured as soluble solids content, SSC) in peach/nectarine assessed at four trials from three US peach-breeding programs as a case study, we evaluated the hypotheses that (i) complex data from multiple breeding programs can be connected using GBLUP models to improve the knowledge of G × E for breeding and deployment and (ii) accounting for a known large-effect quantitative trait locus (QTL) improves the prediction accuracy. Following a structured strategy using univariate and multivariate models containing additive and dominance genomic effects on SSC, a model that included a previously detected QTL and background genomic effects was a significantly better fit than a genome-wide model with completely anonymous markers. Estimates of an individual’s narrow-sense and broad-sense heritability for SSC were high (0.57–0.73 and 0.66–0.80, respectively), with 19–32% of total genomic variance explained by the QTL. Genome-wide dominance effects and QTL effects were stable across environments. Significant G × E was detected for background genome effects, mostly due to the low correlation of these effects across seasons within a particular trial. The expected prediction accuracy, estimated from the linear model, was higher than the realised prediction accuracy estimated by cross-validation, suggesting that these two parameters measure different qualities of the prediction models. While prediction accuracy was improved in some cases by combining data across trials, particularly when phenotypic data for untested individuals were available from other trials, this improvement was not consistent. This study confirms that complex data can be combined into a single analysis using GBLUP methods to improve understanding of G × E and also incorporate known QTL effects. In addition, the study generated baseline information to account for population structure in genomic prediction models in horticultural crop improvement.

Dataset Information

Unique expression, processing regulation, and regulatory network of peach (Prunus persica) miRNAs.

Publications

Unique expression, processing regulation, and regulatory network of peach (Prunus persica) miRNAs.

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