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Wheat is one of the most important food sources on Earth. MicroRNAs (miRNAs) play important roles in wheat productivity. To identify wheat miRNAs as well as their expression profiles under drought condition, we constructed and sequenced small RNA (sRNA) libraries from the leaves and roots of three wheat cultivars (Kukri, RAC875 and Excalibur) under water and drought conditions. A total of 636 known miRNAs and 294 novel miRNAs were identified, of which 34 miRNAs were tissue- or cultivar-specific. Among these, 314 were significantly regulated under drought conditions. miRNAs that were drought-regulated in all cultivars displayed notably higher expression than those that responded in a cultivar-specific manner. Cultivar-specific drought response miRNAs were mainly detected in roots and showed significantly different drought regulations between cultivars. By using wheat degradome library, 6619 target genes were identified. Many target genes were strongly enriched for protein domains, such as MEKHLA, that play roles in drought response. Targeting analysis showed that drought-downregulated miRNAs targeted more genes than drought-upregulated miRNAs. Furthermore, such genes had more important functions. Additionally, the genes targeted by drought-downregulated miRNAs had multiple interactions with each other, while the genes targeted by drought-upregulated miRNAs had no interactions. Our data provide valuable information on wheat miRNA expression profiles and potential functions in different tissues, cultivars and drought conditions. [ABSTRACT FROM AUTHOR]

Endogenous microRNAs (miRNAs) play pivotal roles in plant development and responses to various biotic or abiotic stresses. Up to now, more than 500 maize miRNAs have been identified. However, functions of these identified miRNAs remained largely unknown due mainly to the lack of rapid and reliable tools. We previously reported a cucumber mosaic virus strain ZMBJ (ZMBJ-CMV)-based gene silencing vector for rapid and efficient gene function studies in maize lines with agronomical importance. Because ZMBJ-CMV induces very mild disease symptoms but strong gene silencing in maize, we decided to further modify this vector to suppress miRNA expressions in maize. The newly developed ZMBJ-CMV-2bN81-STTM vector expresses a short tandem target mimic (STTM) containing two target-mimic sequences separated by a short spacer sequence. Our results showed that ZMBJ-CMV-2bN81-STTM can be used to investigate miRNA function in Nicotiana benthamiana and maize seedlings. The ZMBJ-CMV-2bN81-STTM-based downregulation of Nbe-miR165/166 or Nbe-miR159 induced specific and strong miRNA-sequestering phenotypes, and increased the expressions of their predicted target genes. For maize, the ZMBJ-CMV-2bN81-STTM based downregulation of zma-miR167 or zma-miR482 caused a decrease of lateral roots growth and a plant stunting phenotypes, respectively. In both cases, the target genes of zma-miR167- or zma-miR482 were increased significantly. Thus, we consider ZMBJ-CMV based VbMS system as a useful tool for high-throughput investigations of miRNA functions in maize. [ABSTRACT FROM AUTHOR]

Peripheral induction of regulatory T (Treg) cells provides essential protection from inappropriate immune responses. CD4+ T cells that lack endogenous mi RNAs are impaired to differentiate into Treg cells, but the relevant mi RNAs are unknown. We performed an overexpression screen with T-cell-expressed mi RNAs in naive mouse CD4+ T cells undergoing Treg differentiation. Among 130 candidates, the screen identified 29 mi RNAs with a negative and 10 mi RNAs with a positive effect. Testing reciprocal Th17 differentiation revealed specific functions for miR-100, miR-99a and miR-10b, since all of these promoted the Treg and inhibited the Th17 program without impacting on viability, proliferation and activation. miR-99a cooperated with miR-150 to repress the expression of the Th17-promoting factor mTOR. The comparably low expression of miR-99a was strongly increased by the Treg cell inducer 'retinoic acid', and the abundantly expressed miR-150 could only repress Mtor in the presence of miR-99a. Our data suggest that induction of Treg cell differentiation is regulated by a mi RNA network, which involves cooperation of constitutively expressed as well as inducible mi RNAs. [ABSTRACT FROM AUTHOR]

MicroRNAs (miRNAs) are endogenous small RNAs that can regulate target mRNAs by binding to their 3′-UTRs. A single miRNA can regulate many mRNA targets, and several miRNAs can regulate a single mRNA. These have been reported to be involved in a variety of functions, including developmental transitions, neuronal patterning, apoptosis, adipogenesis metabolism and hematopoiesis in different organisms. Many oncogenes and tumor suppressor genes are regulated by miRNAs. Studies conducted in the past few years have demonstrated the possible association between miRNAs and several human malignancies and infectious diseases. In this article, we have focused on the mechanism of miRNA biogenesis and the role of miRNAs in human health and disease. [ABSTRACT FROM AUTHOR]

MicroRNAs (miRNAs) are short, single-stranded, non-coding ribonucleic acid (RNA) molecules, which are involved in the regulation of main biological processes, such as apoptosis or cell proliferation and differentiation, through sequence-specific interaction with target mRNAs. In this study, we propose a workflow for predicting miRNAs function by analyzing the structure of the network of their target genes. This workflow was applied to study the functional role of miR-375 in the heart muscle (myocardium), since this miRNA was previously shown to be associated with heart diseases, and data on its function in the myocardium are mostly unclear. We identified PIK3CA, RHOA, MAPK3, PAFAH1B1, CTNNB1, MYC, PRKCA, ERBB2, and CDC42 as key genes in the miR-375 regulated network and predicted the possible function of miR-375 in the heart muscle, consisting mainly in the regulation of the Rho-GTPases-dependent signaling pathways. We implemented our algorithm for miRNA function prediction into a Python module, which is available at GitHub. [ABSTRACT FROM AUTHOR]