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Prediction and Characterization of miRNA/Target Pairs in Non-Model Plants Using RNA-seq.

ABSTRACT: Plant microRNAs (miRNAs) are ∼20- to 24-nucleotide small RNAs that post-transcriptionally regulate gene expression of mRNA targets. Here, we present a workflow to characterize the miRNA transcriptome of a non-model plant, focusing on miRNAs and targets that are differentially expressed under one experimental treatment. We cover RNA-seq experimental design to create paired small RNA and mRNA libraries and perform quality control of raw data, de novo mRNA transcriptome assembly and annotation, miRNA prediction, differential expression, target identification, and functional enrichment analysis. Additionally, we include validation of differential expression and miRNA-induced target cleavage using qRT-PCR and modified RNA ligase-mediated 5' rapid amplification of cDNA ends, respectively. Our procedure relies on freely available software and web resources. It is intended for users that lack programming skills but can navigate a command-line interface. To enable an understanding of formatting requirements and anticipated results, we provide sample RNA-seq data and key input/output files for each stage. © 2019 The Authors. This is an open access article under the terms of the Creative Commons Attribution-NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.

SUBMITTER: Neller KCM

PROVIDER: S-EPMC9285518 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundDeep-sequencing has enabled the identification of large numbers of miRNAs and siRNAs, making the high-throughput target identification a main limiting factor in defining their function. In plants, several tools have been developed to predict targets, majority of them being trained on Arabidopsis datasets. An extensive and systematic evaluation has not been made for their suitability for predicting targets in species other than Arabidopsis. Nor, these have not been evaluated for their suitability for high-throughput target prediction at genome level.ResultsWe evaluated the performance of 11 computational tools in identifying genome-wide targets in Arabidopsis and other plants with procedures that optimized score-cutoffs for estimating targets. Targetfinder was most efficient [89% 'precision' (accuracy of prediction), 97% 'recall' (sensitivity)] in predicting 'true-positive' targets in Arabidopsis miRNA-mRNA interactions. In contrast, only 46% of true positive interactions from non-Arabidopsis species were detected, indicating low 'recall' values. Score optimizations increased the 'recall' to only 70% (corresponding 'precision': 65%) for datasets of true miRNA-mRNA interactions in species other than Arabidopsis. Combining the results of Targetfinder and psRNATarget delivers high true positive coverage, whereas the intersection of psRNATarget and Tapirhybrid outputs deliver highly 'precise' predictions. The large number of 'false negative' predictions delivered from non-Arabidopsis datasets by all the available tools indicate the diversity in miRNAs-mRNA interaction features between Arabidopsis and other species. A subset of miRNA-mRNA interactions differed significantly for features in seed regions as well as the total number of matches/mismatches.ConclusionAlthough, many plant miRNA target prediction tools may be optimized to predict targets with high specificity in Arabidopsis, such optimized thresholds may not be suitable for many targets in non-Arabidopsis species. More importantly, non-conventional features of miRNA-mRNA interaction may exist in plants indicating alternate mode of miRNA target recognition. Incorporation of these divergent features would enable next-generation of algorithms to better identify target interactions.

Project description:THE SLA (swine leukocyte antigen, MHC: SLA) genes are the most important determinants of immune, infectious disease and vaccine response in pigs; several genetic associations with immunity and swine production traits have been reported. However, most of the current knowledge on SLA is limited to gene coding regions. MicroRNAs (miRNAs) are small molecules that post-transcriptionally regulate the expression of a large number of protein-coding genes in metazoans, and are suggested to play important roles in fine-tuning immune mechanisms and disease responses. Polymorphisms in either miRNAs or their gene targets may have a significant impact on gene expression by abolishing, weakening or creating miRNA target sites, possibly leading to phenotypic variation. We explored the impact of variants in the 3'-UTR miRNA target sites of genes within the whole SLA region. The combined predictions by TargetScan, PACMIT and TargetSpy, based on different biological parameters, empowered the identification of miRNA target sites and the discovery of polymorphic miRNA target sites (poly-miRTSs). Predictions for three SLA genes characterized by a different range of sequence variation provided proof of principle for the analysis of poly-miRTSs from a total of 144 M RNA-Seq reads collected from different porcine tissues. Twenty-four novel SNPs were predicted to affect miRNA-binding sites in 19 genes of the SLA region. Seven of these genes (SLA-1, SLA-6, SLA-DQA, SLA-DQB1, SLA-DOA, SLA-DOB and TAP1) are linked to antigen processing and presentation functions, which is reminiscent of associations with disease traits reported for altered miRNA binding to MHC genes in humans. An inverse correlation in expression levels was demonstrated between miRNAs and co-expressed SLA targets by exploiting a published dataset (RNA-Seq and small RNA-Seq) of three porcine tissues. Our results support the resource value of RNA-Seq collections to identify SNPs that may lead to altered miRNA regulation patterns.

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Prediction and Characterization of miRNA/Target Pairs in Non-Model Plants Using RNA-seq.

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