Functional Characterization of Drosophila microRNAs by a Novel in Vivo Library

Animal microRNAs (miRNA) are implicated in the control of nearly all cellular functions. Due to high sequence redundancy within the miRNA gene pool, loss of most of these 21- to 24-bp long RNAs individually does not cause a phenotype. Thus, only very few miRNAs have been associated with clear functional roles. We constructed a transgenic UAS-miRNA library in Drosophila melanogaster that contains 180 fly miRNAs. This library circumvents the redundancy issues by facilitating the controlled misexpression of individual miRNAs and is a useful tool to complement loss-of-function approaches. Demonstrating the effectiveness of our library, 78 miRNAs induced clear phenotypes. Most of these miRNAs were previously unstudied. Furthermore, we present a simple system to create GFP sensors to monitor miRNA expression and test direct functional interactions in vivo. Finally, we focus on the miR-92 family and identify a direct target gene that is responsible for the specific wing phenotype induced by the misexpression of miR-92 family members.     

Host microRNAs exhibit differential propensity to interact with SARS-CoV-2 and variants of concern

A significant number of SARS-CoV-2-infected individuals naturally overcome viral infection, suggesting the existence of a potent endogenous antiviral mechanism. As an innate defense mechanism, microRNA (miRNA) pathways in mammals have evolved to restrict viruses, besides regulating endogenous mRNAs. In this study, we systematically examined the complete repertoire of human miRNAs for potential binding sites on SARS-CoV-2 Wuhan-Hu-1, Beta, Delta, and Omicron. Human miRNA and viral genome interaction were analyzed using RNAhybrid 2.2 with stringent parameters to identify highly bonafide miRNA targets. Using publicly available data, we filtered for miRNAs expressed in lung epithelial cells/tissue and oral keratinocytes, concentrating on the miRNAs that target SARS-CoV-2 S protein mRNAs. Our results show a significant loss of human miRNA and SARS-CoV-2 interactions in Omicron (130 miRNAs) compared to Wuhan-Hu-1 (271 miRNAs), Beta (279 miRNAs), and Delta (275 miRNAs). In particular, hsa-miR-3150b-3p and hsa-miR-4784 show binding affinity for S protein of Wuhan strain but not Beta, Delta, and Omicron. Loss of miRNA binding sites on N protein was also observed for Omicron. Through Ingenuity Pathway Analysis (IPA), we examined the experimentally validated and highly predicted functional role of these miRNAs. We found that hsa-miR-3150b-3p and hsa-miR-4784 have several experimentally validated or highly predicted target genes in the Toll-like receptor, IL-17, Th1, Th2, interferon, and coronavirus pathogenesis pathways. Focusing on the coronavirus pathogenesis pathway, we found that hsa-miR-3150b-3p and hsa-miR-4784 are highly predicted to target MAPK13. Exploring miRNAs to manipulate viral genome/gene expression can provide a promising strategy with successful outcomes by targeting specific VOCs.     

Lessons from microRNA mutants in worms,flies and mice

It is apparent that microRNAs (miRNAs) are important components in the regulation of genetic networks in many biological contexts. Based on computational analysis, typical miRNAs are inferred to have tens to hundreds of conserved targets. Many miRNA-target interactions have been validated by various means, including heterologous tests in cultured cells and gain-of-function approaches that can yield striking phenotypes in whole animals. However, these strategies do not report on the endogenous importance of such miRNA activities. Likewise, studies of miRNA pathway mutants can suggest an endogenous role for miRNAs in a given setting, but do not identify roles for specific miRNAs. Therefore, these approaches must be complemented with the analysis of miRNA mutant alleles. In this review, we describe some of the lessons learned from studying miRNA gene deletions in worms, flies and mice, and discuss their implications for the control of endogenous regulatory networks.     

Inference of miRNA targets using evolutionary conservation and pathway analysis

Background

MicroRNAs have emerged as important regulatory genes in a variety of cellular processes and, in recent years, hundreds of such genes have been discovered in animals. In contrast, functional annotations are available only for a very small fraction of these miRNAs, and even in these cases only partially.

Results

We developed a general Bayesian method for the inference of miRNA target sites, in which, for each miRNA, we explicitly model the evolution of orthologous target sites in a set of related species. Using this method we predict target sites for all known miRNAs in flies, worms, fish, and mammals. By comparing our predictions in fly with a reference set of experimentally tested miRNA-mRNA interactions we show that our general method performs at least as well as the most accurate methods available to date, including ones specifically tailored for target prediction in fly. An important novel feature of our model is that it explicitly infers the phylogenetic distribution of functional target sites, independently for each miRNA. This allows us to infer species-specific and clade-specific miRNA targeting. We also show that, in long human 3' UTRs, miRNA target sites occur preferentially near the start and near the end of the 3' UTR. To characterize miRNA function beyond the predicted lists of targets we further present a method to infer significant associations between the sets of targets predicted for individual miRNAs and specific biochemical pathways, in particular those of the KEGG pathway database. We show that this approach retrieves several known functional miRNA-mRNA associations, and predicts novel functions for known miRNAs in cell growth and in development.

Conclusion

We have presented a Bayesian target prediction algorithm without any tunable parameters, that can be applied to sequences from any clade of species. The algorithm automatically infers the phylogenetic distribution of functional sites for each miRNA, and assigns a posterior probability to each putative target site. The results presented here indicate that our general method achieves very good performance in predicting miRNA target sites, providing at the same time insights into the evolution of target sites for individual miRNAs. Moreover, by combining our predictions with pathway analysis, we propose functions of specific miRNAs in nervous system development, inter-cellular communication and cell growth. The complete target site predictions as well as the miRNA/pathway associations are accessible on the ElMMo web server.     

Alteration of tomato microRNAs expression during fruit development upon Cucumber mosaic virus and Tomato aspermy virus infection

The economic importance of Solanaceae plant species is well documented, and tomato has become a model for fleshy fruit development and ripening studies. Plant microRNAs (miRNAs) are small endogenous RNAs that are involved in a variety of activities including plant development, signal transduction and protein degradation, as well as response to environment stress and pathogen invasion. Here in this study, we aimed at quantifying the expression alterations of nine miRNAs and target mRNAs in tomato flower and fruit development upon Cucumber mosaic virus (CMV) and Tomato aspermy virus infections. Three different CMV strains CMV-Fny, CMV-FnyΔ2b and CMV-Fny-satT1 were used in our investigation, and the miRNA/mRNA expression alterations were analyzed by real-time quantitative RT-PCR. The results shown the levels of several miRNA/mRNA pairs were increased upon virus infections. However, the increased level of individual miRNA differed for different virus strains, reflecting differences in severity of symptom phenotypes. The altered expression patterns of these miRNA/mRNA pairs and their predicted functions indicate the possible roles in flower and fruit development, and provide experimental data for understanding the miRNA-mediated phenotype alterations in tomato fruit.     

Bayesian inference of MicroRNA targets from sequence and expression data.

MicroRNAs (miRNAs) regulate a large proportion of mammalian genes by hybridizing to targeted messenger RNAs (mRNAs) and down-regulating their translation into protein. Although much work has been done in the genome-wide computational prediction of miRNA genes and their target mRNAs, an open question is how to efficiently obtain functional miRNA targets from a large number of candidate miRNA targets predicted by existing computational algorithms. In this paper, we propose a novel Bayesian model and learning algorithm, GenMiR++ (Generative model for miRNA regulation), that accounts for patterns of gene expression using miRNA expression data and a set of candidate miRNA targets. A set of high-confidence functional miRNA targets are then obtained from the data using a Bayesian learning algorithm. Our model scores 467 high-confidence miRNA targets out of 1,770 targets obtained from TargetScanS in mouse at a false detection rate of 2.5%: several confirmed miRNA targets appear in our high-confidence set, such as the interactions between miR-92 and the signal transduction gene MAP2K4, as well as the relationship between miR-16 and BCL2, an anti-apoptotic gene which has been implicated in chronic lymphocytic leukemia. We present results on the robustness of our model showing that our learning algorithm is not sensitive to various perturbations of the data. Our high-confidence targets represent a significant increase in the number of miRNA targets and represent a starting point for a global understanding of gene regulation.     

miRConnect: identifying effector genes of miRNAs and miRNA families in cancer cells

micro(mi)RNAs are small non-coding RNAs that negatively regulate expression of most mRNAs. They are powerful regulators of various differentiation stages, and the expression of genes that either negatively or positively correlate with expressed miRNAs is expected to hold information on the biological state of the cell and, hence, of the function of the expressed miRNAs. We have compared the large amount of available gene array data on the steady state system of the NCI60 cell lines to two different data sets containing information on the expression of 583 individual miRNAs. In addition, we have generated custom data sets containing expression information of 54 miRNA families sharing the same seed match. We have developed a novel strategy for correlating miRNAs with individual genes based on a summed Pearson Correlation Coefficient (sPCC) that mimics an in silico titration experiment. By focusing on the genes that correlate with the expression of miRNAs without necessarily being direct targets of miRNAs, we have clustered miRNAs into different functional groups. This has resulted in the identification of three novel miRNAs that are linked to the epithelial-to-mesenchymal transition (EMT) in addition to the known EMT regulators of the miR-200 miRNA family. In addition, an analysis of gene signatures associated with EMT, c-MYC activity, and ribosomal protein gene expression allowed us to assign different activities to each of the functional clusters of miRNAs. All correlation data are available via a web interface that allows investigators to identify genes whose expression correlates with the expression of single miRNAs or entire miRNA families. miRConnect.org will aid in identifying pathways regulated by miRNAs without requiring specific knowledge of miRNA targets.     

Complementary miRNA pairs suggest a regulatory role for miRNA:miRNA duplexes

microRNAs (miRNAs) are 21-22-nucleotide noncoding RNAs that are widely believed to regulate complementary mRNA targets. However, due to the modest amount of pairing involved, only a few out of the hundreds of known animal miRNAs have thus far been connected to mRNA targets. Here, we considered the possibility that miRNAs might regulate non-mRNA targets, namely other miRNAs. To do so, we conducted a systematic assessment of the nearly complete catalogs of animal miRNAs for potential miRNA:miRNA complements. Our analysis uncovered several compelling examples that strongly suggest a function for miRNA duplexes, thus adding a potential layer of regulatory sophistication to the small RNA world. Interestingly, the most striking examples involve miRNAs complementary to members of the K-box family and Brd-box family, two classes of miRNAs previously implicated in regulation of Notch target genes. We emphasize that patterns of nucleotide constraint indicate that miRNA complementarity is not a simple consequence of miRNA:miRNA* complementarity; however, our findings do suggest that the potential regulatory consequences of the latter also deserve investigation.     

Identification of human miRNA precursors that resemble box C/D snoRNAs

There are two main classes of small nucleolar RNAs (snoRNAs): the box C/D snoRNAs and the box H/ACA snoRNAs that function as guide RNAs to direct sequence-specific modification of rRNA precursors and other nucleolar RNA targets. A previous computational and biochemical analysis revealed a possible evolutionary relationship between miRNA precursors and some box H/ACA snoRNAs. Here, we investigate a similar evolutionary relationship between a subset of miRNA precursors and box C/D snoRNAs. Computational analyses identified 84 intronic miRNAs that are encoded within either box C/D snoRNAs, or in precursors showing similarity to box C/D snoRNAs. Predictions of the folded structures of these box C/D snoRNA-like miRNA precursors resemble the structures of known box C/D snoRNAs, with the boxes C and D often in close proximity in the folded molecule. All five box C/D snoRNA-like miRNA precursors tested (miR-27b, miR-16-1, mir-28, miR-31 and let-7g) bind to fibrillarin, a specific protein component of functional box C/D snoRNP complexes. The data suggest that a subset of small regulatory RNAs may have evolved from box C/D snoRNAs.