Identification of MiRNA from Eggplant (Solanum melongena L.) by Small RNA Deep Sequencing and Their Response to Verticillium dahliae Infection

MiRNAs are a class of non-coding small RNAs that play important roles in the regulation of gene expression. Although plant miRNAs have been extensively studied in model systems, less is known in other plants with limited genome sequence data, including eggplant (Solanum melongena L.). To identify miRNAs in eggplant and their response to Verticillium dahliae infection, a fungal pathogen for which clear understanding of infection mechanisms and effective cure methods are currently lacking, we deep-sequenced two small RNA (sRNA) libraries prepared from mock-infected and infected seedlings of eggplants. Specifically, 30,830,792 reads produced 7,716,328 unique miRNAs representing 99 known miRNA families that have been identified in other plant species. Two novel putative miRNAs were predicted with eggplant ESTs. The potential targets of the identified known and novel miRNAs were also predicted based on sequence homology search. It was observed that the length distribution of obtained sRNAs and the expression of 6 miRNA families were obviously different between the two libraries. These results provide a framework for further analysis of miRNAs and their role in regulating plant response to fungal infection and Verticillium wilt in particular.     

Identification and verification of microRNA in wheat (Triticum aestivum)

MicroRNAs (miRNAs) are small, endogenous RNAs that regulate gene expression in both plants and animals. A large number of miRNAs has been identified from various animals and model plant species such as Arabidopsis thaliana and rice (Oryza sativa); however, characteristics of wheat (Triticum aestivum) miRNAs are poorly understood. Here, computational identification of miRNAs from wheat EST sequences was preformed by using the in-house program GenomicSVM, a prediction model for miRNAs. This study resulted in the discovery of 79 miRNA candidates. Nine out of 22 miRNA representatives randomly selected from the 79 candidates were experimentally validated with Northern blotting, indicating that prediction accuracy is about 40%. For the 9 validated miRNAs, 59 wheat ESTs were predicted as their putative targets. Electronic supplementary material The online version of this article (doi:) contains supplementary material, which is available to authorized users. Weibo Jin and Nannan Li contributed equally to the work.     

Identification and validation of Asteraceae miRNAs by the expressed sequence tag analysis

MicroRNAs (miRNAs) are small non-coding RNA molecules that play a vital role in the regulation of gene expression. Despite their identification in hundreds of plant species, few miRNAs have been identified in the Asteraceae, a large family that comprises approximately one tenth of all flowering plants. In this study, we used the expressed sequence tag (EST) analysis to identify potential conserved miRNAs and their putative target genes in the Asteraceae. We applied quantitative Real-Time PCR (qRT-PCR) to confirm the expression of eight potential miRNAs in Carthamus tinctorius and Helianthus annuus. We also performed qRT-PCR analysis to investigate the differential expression pattern of five newly identified miRNAs during five different cotyledon growth stages in safflower. Using these methods, we successfully identified and characterized 151 potentially conserved miRNAs, belonging to 26 miRNA families, in 11 genus of Asteraceae. EST analysis predicted that the newly identified conserved Asteraceae miRNAs target 130 total protein-coding ESTs in sunflower and safflower, as well as 433 additional target genes in other plant species. We experimentally confirmed the existence of seven predicted miRNAs, (miR156, miR159, miR160, miR162, miR166, miR396, and miR398) in safflower and sunflower seedlings. We also observed that five out of eight miRNAs are differentially expressed during cotyledon development. Our results indicate that miRNAs may be involved in the regulation of gene expression during seed germination and the formation of the cotyledons in the Asteraceae. The findings of this study might ultimately help in the understanding of miRNA-mediated gene regulation in important crop species.     

人源microRNA前体的全基因组预测

microRNA（miRNA）是一类不编码蛋白的调控小分子RNA,在真核生物中发挥着广泛而重要的调控功能.由于miRNA的表达具有时空特异性,因而通过计算方法预测miRNA而后有针对性的实验验证是miRNA发现的一条重要途径.降低假阳性率是miRNA预测方法面临的重要挑战.本研究采用集成学习方法构建预测miRNA前体的分类器SVMbagging,对训练集、测试集和独立测试集的结果表明,本研究的方法性能稳健、假阳性率低,具有很好的泛化能力,尤其是当阈值取0.9时,特异性高达99.90%,敏感性在26%以上,适合于全基因组预测.采用SVMbagging在人全基因组中预测miRNA前体,当取阈值0.9时,得到14933个可能的miRNA前体.通过与高通量小RNA测序数据的比较,发现其中4481个miRNA前体具有完全匹配的小RNA序列,与理论估计的真阳性数值非常接近.最后,对32个可能的miRNA进行实验验证,确定其中2条为真实的miRNA.     

SCMFMDA: Predicting microRNA-disease associations based on similarity constrained matrix factorization

miRNAs belong to small non-coding RNAs that are related to a number of complicated biological processes. Considerable studies have suggested that miRNAs are closely associated with many human diseases. In this study, we proposed a computational model based on Similarity Constrained Matrix Factorization for miRNA-Disease Association Prediction (SCMFMDA). In order to effectively combine different disease and miRNA similarity data, we applied similarity network fusion algorithm to obtain integrated disease similarity (composed of disease functional similarity, disease semantic similarity and disease Gaussian interaction profile kernel similarity) and integrated miRNA similarity (composed of miRNA functional similarity, miRNA sequence similarity and miRNA Gaussian interaction profile kernel similarity). In addition, the L₂ regularization terms and similarity constraint terms were added to traditional Nonnegative Matrix Factorization algorithm to predict disease-related miRNAs. SCMFMDA achieved AUCs of 0.9675 and 0.9447 based on global Leave-one-out cross validation and five-fold cross validation, respectively. Furthermore, the case studies on two common human diseases were also implemented to demonstrate the prediction accuracy of SCMFMDA. The out of top 50 predicted miRNAs confirmed by experimental reports that indicated SCMFMDA was effective for prediction of relationship between miRNAs and diseases.