Comprehensive, integrative genomic analysis of microRNA expression profiles in different tissues of two wheat cultivars with different traits.

Wheat is one of the most important food sources on Earth. MicroRNAs (miRNA) play important roles in wheat productivity. To identify wheat miRNAs, we constructed and sequenced sRNA libraries from leaves and roots of two wheat cultivars (RAC875 and Kukri) with many different traits. Given that available miRNA wheat complement in the plant-specific database PmiREN ( https://pmiren.com ) does not include root tissues and root-associated miRNAs might thus be missing, we performed first the prediction of novel miRNAs using the sRNAbench tool. We found a total of 150 putatively novel miRNA genes with expression of both arms from 289 unique mature sequences and nearly 30% of all miRNA reads in roots corresponded to novel miRNAs. In contrast, this figure in leaves dropped to under 3%, confirming the undersampling of roots in the complement of known miRNAs. By using 120 publicly available wheat datasets, 598 Zea mays small RNA libraries, 64 plant species genomes, wheat degradome library, and functional enrichment analysis, a subset of novel miRNAs were confirmed as bona-fide miRNAs. Of the total 605 miRNAs identified in this study inclusive of 316 known miRNAs, 528 miRNAs were shared by both cultivars, 429 miRNAs were shared by both root tissues and 329 miRNAs were shared by both leaf tissues. In addition, 32 miRNAs were specific to Kukri while 45 miRNAs were specific to RAC875. These miRNAs had diverse functions, such as regulation of gene transcription, protein translation, energy metabolism, and cell cycle progression. Our data provide a genome-wide miRNA expression profile in these two wheat cultivars and help functional studies of wheat genomics.

Machine Learning Using Gene-Sets to Infer miRNA Function.

miRNA are regulators of cell phenotype, and there is clear evidence that these small posttranscriptional modifiers of gene expression are involved in defining a cellular response across states of development and disease. Classical methods for elucidating the repressive effect of a miRNA on its targets involve controlling for the many factors influencing miRNA action, and this can be achieved in cell lines, but misses tissue and organism level context which are key to a miRNA function. Also, current technology to carry out this validation is limited in both generalizability and throughput. Methodologies with greater scalability and rapidity are required to better understand the function of these important species of RNA. To this end, there is an increasing store of RNA expression level data incorporating both miRNA and mRNA, and in this chapter, we describe how to use machine learning and gene-sets to translate the knowledge of phenotype defined by mRNA to putative roles for miRNA. We outline our approach to this process and highlight how it was done for our miRNA annotation of the hallmarks of cancer using the Cancer Genome Atlas (TCGA) dataset. The concepts we present are applicable across datasets and phenotypes, and we highlight potential pitfalls and challenges that may be faced as they are used.

From miRNA Target Gene Network to miRNA Function: miR-375 Might Regulate Apoptosis and Actin Dynamics in the Heart Muscle via Rho-GTPases-Dependent Pathways.

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.

Induced miR-99a expression represses Mtor cooperatively with miR-150 to promote regulatory T-cell differentiation.

Peripheral induction of regulatory T (Treg) cells provides essential protection from inappropriate immune responses. CD4(+) T cells that lack endogenous miRNAs are impaired to differentiate into Treg cells, but the relevant miRNAs are unknown. We performed an overexpression screen with T-cell-expressed miRNAs in naive mouse CD4(+) T cells undergoing Treg differentiation. Among 130 candidates, the screen identified 29 miRNAs with a negative and 10 miRNAs 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 miRNA network, which involves cooperation of constitutively expressed as well as inducible miRNAs.

Small RNA and degradome profiling reveals a role for miRNAs and their targets in the developing fibers of Gossypium barbadense.

microRNAs (miRNAs) are 20-24 nucleotide non-coding small RNAs that play important roles in plant development. The stages of cotton fiber development include initiation, elongation, secondary wall thickening (SWT) and maturation. We constructed seven fiber RNA libraries representing the initiation, elongation and SWT stages. In total, 47 conserved miRNA families and seven candidate miRNAs were profiled using small RNA sequencing. Northern blotting and real-time polymerase chain reaction (PCR) analyses revealed the dynamic expression of miRNAs during fiber development. In addition, 140 targets of 30 conserved miRNAs and 38 targets of five candidate miRNAs were identified through degradome sequencing. Analysis of correlated expression between miRNAs and their targets demonstrated that specific miRNAs suppressed the expression of transcription factors, SBP and MYB, a leucine-rich receptor-like protein kinase, a pectate lyase, α-tubulin, a UDP-glucuronic acid decarboxylase and cytochrome C oxidase subunit 1 to affect fiber development. Histochemical analyses detected the biological activity of miRNA156/157 in ovule and fiber development. Suppressing miRNA156/157 function resulted in the reduction of mature fiber length, illustrating that miRNA156/157 plays an essential role in fiber elongation.

Genome-Wide Analysis of microRNA Expression Profile in Roots and Leaves of Three Wheat Cultivars under Water and Drought Conditions.

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.

MicroRNA-760 resists ambient PM2.5-induced apoptosis in human bronchial epithelial cells through elevating heme-oxygenase 1 expression.

PM2.5 (particles matter smaller aerodynamic diameter of 2.5 µm) exposure, a major environmental risk factor for the global burden of diseases, is associated with high risks of respiratory diseases. Heme-oxygenase 1 (HMOX1) is one of the major molecular antioxidant defenses to mediate cytoprotective effects against diverse stressors, including PM2.5-induced toxicity; however, the regulatory mechanism of HMOX1 expression still needs to be elucidated. In this study, using PM2.5 as a typical stressor, we explored whether microRNAs (miRNAs) might modulate HMOX1 expression in lung cells. Systematic bioinformatics analysis showed that seven miRNAs have the potentials to target HMOX1 gene. Among these, hsa-miR-760 was identified as the most responsive miRNA to PM2.5 exposure. More importantly, we revealed a "non-conventional" miRNA function in hsa-miR-760 upregulating HMOX1 expression, by targeting the coding region and interacting with YBX1 protein. In addition, we observed that exogenous hsa-miR-760 effectively elevated HMOX1 expression, reduced the reactive oxygen agents (ROS) levels, and rescued the lung cells from PM2.5-induced apoptosis. Our results revealed that hsa-miR-760 might play an important role in protecting lung cells against PM2.5-induced toxicity, by elevating HMOX1 expression, and offered new clues to elucidate the diverse functions of miRNAs.

Analyses of MiRNA Functions in Maize Using a Newly Developed ZMBJ-CMV-2bN81-STTM Vector.

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.

Predicting Gene Ontology Function of Human MicroRNAs by Integrating Multiple Networks.

MicroRNAs (miRNAs) have been demonstrated to play significant biological roles in many human biological processes. Inferring the functions of miRNAs is an important strategy for understanding disease pathogenesis at the molecular level. In this paper, we propose an integrated model, PmiRGO, to infer the gene ontology (GO) functions of miRNAs by integrating multiple data sources, including the expression profiles of miRNAs, miRNA-target interactions, and protein-protein interactions (PPI). PmiRGO starts by building a global network consisting of three networks. Then, it employs DeepWalk to learn latent representations as network features of the global heterogeneous network. Finally, the SVM-based models are applied to label the GO terms of miRNAs. The experimental results show that PmiRGO has a significantly better performance than existing state-of-the-art methods in terms of F max . A case study further demonstrates the feasibility of PmiRGO to annotate the potential functions of miRNAs.

Bovine Foamy Virus: Shared and Unique Molecular Features In Vitro and In Vivo.

The retroviral subfamily of Spumaretrovirinae consists of five genera of foamy (spuma) viruses (FVs) that are endemic in some mammalian hosts [1]. Closely related species may be susceptible to the same or highly related FVs. FVs are not known to induce overt disease and thus do not pose medical problems to humans and livestock or companion animals. A robust lab animal model is not available or is a lab animal a natural host of a FV. Due to this, research is limited and often focused on the simian FVs with their well-established zoonotic potential. The authors of this review and their groups have conducted several studies on bovine FV (BFV) in the past with the intention of (i) exploring the risk of zoonotic infection via beef and raw cattle products, (ii) studying a co-factorial role of BFV in different cattle diseases with unclear etiology, (iii) exploring unique features of FV molecular biology and replication strategies in non-simian FVs, and (iv) conducting animal studies and functional virology in BFV-infected calves as a model for corresponding studies in primates or small lab animals. These studies gained new insights into FV-host interactions, mechanisms of gene expression, and transcriptional regulation, including miRNA biology, host-directed restriction of FV replication, spread and distribution in the infected animal, and at the population level. The current review attempts to summarize these findings in BFV and tries to connect them to findings from other FVs.

New Insight into microRNA Functions in Cancer: Oncogene-microRNA-Tumor Suppressor Gene Network.

Tumorigenesis is a multi-step and complex process with multi-factors involved. Deregulated oncogenes and tumor suppressor genes (TSGs) induced by genetic and epigenetic factors are considered as the driving force in the development and progression of cancer. Besides, microRNAs (miRNAs) act vital roles in tumorigenesis through regulating some oncogenes and TSGs. Interestingly, miRNAs are also regulated by oncogenes and TSGs. Considering the entangled regulation, here we propose a new insight into these regulation relationships in cancer: oncogene-miRNA-TSG network, which further emphasizes roles of miRNA, as well as highlights the network regulation among oncogene, miRNA, and TSG during tumorigenesis. The oncogene-miRNA-TSG network demonstrates that oncogenes and TSGs not only show functional synergy, but also there are regulatory relationships among oncogenes and TSGs during tumorigenesis, which could be mediated by miRNAs. In view of the oncogene-miRNA-TSG network involved in many oncogenes, miRNAs, and TSGs, as well as occurring in various tumor types, the anomaly of this network may be a common event in cancers and participates in tumorigenesis. This hypothesis broadens horizons of molecular mechanisms underlying tumorigenesis, and may provide a new promising venue for the prediction, diagnosis, and even therapy of cancer.

RNA Function Prediction.

Recent studies have shown that a considerable proportion of eukaryotic genomes are transcribed as noncoding RNA (ncRNA), and regulatory ncRNAs have attracted much attention from researchers in many fields, especially of microRNA (miRNA) and long noncoding RNA (lncRNA). However, most ncRNAs are functionally uncharacterized due to the difficulty to accurately identify their targets. In this chapter, we first summarize the most recent advances in ncRNA research and their primary function. We then discuss the current state-of-the-art computational methods for predicting RNA functions, which comprise three different categories: miRNA function prediction approaches using target genes, lncRNA function prediction based on the guilt-by-association principle, and RNA function prediction approaches based on competing endogenous RNA partners. We consider that the application of these techniques can provide valuable functional and mechanistic insights into ncRNAs, and that they are crucial steps in future functional studies.