miRDeepFinder: a miRNA analysis tool for deep sequencing of plant small RNAs

miRDeepFinder is a software package developed to identify and functionally analyze plant microRNAs (miRNAs) and their targets from small RNA datasets obtained from deep sequencing. The functions available in miRDeepFinder include pre-processing of raw data, identifying conserved miRNAs, mining and classifying novel miRNAs, miRNA expression profiling, predicting miRNA targets, and gene pathway and gene network analysis involving miRNAs. The fundamental design of miRDeepFinder is based on miRNA biogenesis, miRNA-mediated gene regulation and target recognition, such as perfect or near perfect hairpin structures, different read abundances of miRNA and miRNA*, and targeting patterns of plant miRNAs. To test the accuracy and robustness of miRDeepFinder, we analyzed a small RNA deep sequencing dataset of Arabidopsis thaliana published in the GEO database of NCBI. Our test retrieved 128 of 131 (97.7%) known miRNAs that have a more than 3 read count in Arabidopsis. Because many known miRNAs are not associated with miRNA*s in small RNA datasets, miRDeepFinder was also designed to recover miRNA candidates without the presence of miRNA*. To mine as many miRNAs as possible, miRDeepFinder allows users to compare mature miRNAs and their miRNA*s with other small RNA datasets from the same species. Cleaveland software package was also incorporated into miRDeepFinder for miRNA target identification using degradome sequencing analysis. Using this new computational tool, we identified 13 novel miRNA candidates with miRNA*s from Arabidopsis and validated 12 of them experimentally. Interestingly, of the 12 verified novel miRNAs, a miRNA named AC1 spans the exons of two genes (UTG71C4 and UGT71C3). Both the mature AC1 miRNA and its miRNA* were also found in four other small RNA datasets. We also developed a tool, ??miRNA primer designer?? to design primers for any type of miRNAs. miRDeepFinder provides a powerful tool for analyzing small RNA datasets from all species, with or without the availability of genome information. miRDeepFinder and miRNA primer designer are freely available at http://www.leonxie.com/DeepFinder.php and at http://www.leonxie.com/miRNAprimerDesigner.php, respectively. A program (called RefFinder: http://www.leonxie.com/referencegene.php) was also developed for assessing the reliable reference genes for gene expression analysis, including miRNAs.     

The role of miRNAs and endogenous siRNAs in maternal‐to‐zygotic reprogramming and the establishment of pluripotency

RNA silencing is a complex of mechanisms that regulate gene expression through small RNA molecules. The microRNA (miRNA) pathway is the most common of these in mammals. Genome‐encoded miRNAs suppress translation in a sequence‐specific manner and facilitate shifts in gene expression during developmental transitions. Here, we discuss the role of miRNAs in oocyte‐to‐zygote transition and in the control of pluripotency. Existing data suggest a common principle involving miRNAs in defining pluripotent and differentiated cells. RNA silencing pathways also rapidly evolve, resulting in many unique features of RNA silencing in different taxonomic groups. This is exemplified in the mouse model of oocyte‐to‐zygote transition, in which the endogenous RNA interference pathway has acquired a novel role in regulating protein‐coding genes, while the miRNA pathway has become transiently suppressed.     

Identification of microRNAs and their corresponding targets involved in the susceptibility interaction of wheat response to Puccinia striiformis f. sp. tritici

MicroRNAs (miRNAs) play very important roles in plant defense responses. However, little is known about their roles in the susceptibility interaction between wheat and Puccinia striiformis f. sp. tritici (Pst). In this study, two miRNA libraries were constructed from the leaves of the cultivar Xingzi 9104 inoculated with the virulent Pst race CYR32 and sterile water, respectively. A total of 1316 miRNA candidates, including 173 known miRNAs that were generated from 98 pre‐miRNAs, were obtained. The remaining 1143 miRNA candidates included 145 conserved and 998 wheat‐specific miRNAs that were generated from 87 and 1088 pre‐miRNAs, respectively. The 173 known and 145 conserved miRNAs were sub‐classified into 63 miRNA families. The target genes of wheat miRNAs were also confirmed using degradome sequencing technology. Most of the annotated target genes were related to signal transduction or energy metabolism. Additionally, we found that miRNAs and their target genes form complicated regulation networks. The expression profiles of miRNAs and their corresponding target genes were further analyzed by quantitative real‐time polymerase chain reaction (qRT‐PCR), and the results indicate that some miRNAs are involved in the compatible wheat‐Pst susceptibility interaction. Importantly, tae‐miR1432 was highly expressed when wheat was challenged with CYR32, and the corresponding target gene, predicted to be a calcium ion‐binding protein, also exhibited upregulated expression but a divergent expression trend. PC‐3P‐7484, a specific wheat miRNA, was highly expressed in the wheat response to Pst infection, while the expression of the corresponding target gene ubiquillin was dramatically downregulated. These data provide the foundation for evaluating the important regulatory roles of miRNAs in wheat‐Pst susceptibility interaction.     

Integrated analysis of miRNA and mRNA expression profiles in response to gut microbiota depletion in the abdomens of female Bactrocera dorsalis

Insect gut microbiota has been reported to participate in regulating host multiple biological processes including metabolism and reproduction. However, the corresponding molecular mechanisms remain largely unknown. Recent studies suggest that microRNAs (miRNAs) are involved in complex interactions between the gut microbiota and the host. Here, we used next-generation sequencing technology to characterize miRNA and mRNA expression profiles and construct the miRNA–gene regulatory network in response to gut microbiota depletion in the abdomens of female Bactrocera dorsalis. A total of 3016 differentially expressed genes (DEGs) and 18 differentially expressed miRNAs (DEMs) were identified. Based on the integrated analysis of miRNA and mRNA sequencing data, 229 negatively correlated miRNA–gene pairs were identified from the miRNA–mRNA network. Gene ontology enrichment analysis indicated that DEMs could target several genes involved in the metabolic process, oxidation–reduction process, oogenesis, and insulin signaling pathway. Finally, real-time quantitative polymerase chain reaction further verified the accuracy of RNA sequencing results. In conclusion, our study provides the profiles of miRNA and mRNA expressions under antibiotics treatment and provides an insight into the roles of miRNAs and their target genes in the interaction between the gut microbiota and its host.