A biophysical miRNA-mRNA interaction model infers canonical and noncanonical targets

MicroRNA-mediated attenuation of branched-chain amino acid catabolism promotes ferroptosis in chronic kidney disease

Chronic kidney disease can develop from kidney injury incident to chemotherapy with cisplatin, which complicates the prognosis of cancer patients. MicroRNAs regulate gene expression by pairing with specific sets of messenger RNAs. Therefore, elucidating direct physical interactions between microRNAs and their target messenger RNAs can help decipher crucial biological processes associated with cisplatin-induced kidney injury. Through intermolecular ligation and transcriptome-wide sequencing, we here identify direct pairs of microRNAs and their target messenger RNAs in the kidney of male mice injured by cisplatin. We find that a group of cisplatin-induced microRNAs can target select messenger RNAs that affect the mitochondrial metabolic pathways in the injured kidney. Specifically, a cisplatin-induced microRNA, miR-429-3p, suppresses the pathway that catabolizes branched-chain amino acids in the proximal tubule, leading to cell death dependent on lipid peroxidation, called ferroptosis. Identification of miRNA-429-3p-mediated ferroptosis stimulation suggests therapeutic potential for modulating the branched-chain amino acid pathway in ameliorating cisplatin-induced kidney injury.

On the rules of engagement for microRNAs targeting protein coding regions

MiRNAs post-transcriptionally repress gene expression by binding to mRNA 3'UTRs, but the extent to which they act through protein coding regions (CDS regions) is less well established. MiRNA interaction studies show a substantial proportion of binding occurs in CDS regions, however sequencing studies show much weaker effects on mRNA levels than from 3'UTR interactions, presumably due to competition from the translating ribosome. Consequently, most target prediction algorithms consider only 3'UTR interactions. However, the consequences of CDS interactions may have been underestimated, with the reporting of a novel mode of miRNA-CDS interaction requiring base pairing of the miRNA 3' end, but not the canonical seed site, leading to repression of translation with little effect on mRNA turnover. Using extensive reporter, western blotting and bioinformatic analyses, we confirm that miRNAs can indeed suppress genes through CDS-interaction in special circumstances. However, in contrast to that previously reported, we find repression requires extensive base-pairing, including of the canonical seed, but does not strictly require base pairing of the 3' miRNA terminus and is mediated through reducing mRNA levels. We conclude that suppression of endogenous genes can occur through miRNAs binding to CDS, but the requirement for extensive base-pairing likely limits the regulatory impacts to modest effects on a small subset of targets.

Virus-Derived Small RNAs and microRNAs in Health and Disease

MicroRNAs (miRNAs) are short noncoding RNAs that can regulate all steps of gene expression (induction, transcription, and translation). Several virus families, primarily double-stranded DNA viruses, encode small RNAs (sRNAs), including miRNAs. These virus-derived miRNAs (v-miRNAs) help the virus evade the host's innate and adaptive immune system and maintain an environment of chronic latent infection. In this review, the functions of the sRNA-mediated virus-host interactions are highlighted, delineating their implication in chronic stress, inflammation, immunopathology, and disease. We provide insights into the latest viral RNA-based research-in silico approaches for functional characterization of v-miRNAs and other RNA types. The latest research can assist toward the identification of therapeutic targets to combat viral infections.

Noncoding RNA circuitry in melanoma onset, plasticity, and therapeutic response

Melanoma, the cancer of the melanocyte, is the deadliest form of skin cancer with an aggressive nature, propensity to metastasize and tendency to resist therapeutic intervention. Studies have identified that the re-emergence of developmental pathways in melanoma contributes to melanoma onset, plasticity, and therapeutic response. Notably, it is well known that noncoding RNAs play a critical role in the development and stress response of tissues. In this review, we focus on the noncoding RNAs, including microRNAs, long non-coding RNAs, circular RNAs, and other small RNAs, for their functions in developmental mechanisms and plasticity, which drive onset, progression, therapeutic response and resistance in melanoma. Going forward, elucidation of noncoding RNA-mediated mechanisms may provide insights that accelerate development of novel melanoma therapies.

The Molecular Mechanism of the TEAD1 Gene and miR-410-5p Affect Embryonic Skeletal Muscle Development: A miRNA-Mediated ceRNA Network Analysis

Muscle development is a complex biological process involving an intricate network of multiple factor interactions. Through the analysis of transcriptome data and molecular biology confirmation, this study aims to reveal the molecular mechanism underlying sheep embryonic skeletal muscle development. The RNA sequencing of embryos was conducted, and microRNA (miRNA)-mediated competitive endogenous RNA (ceRNA) networks were constructed. qRT-PCR, siRNA knockdown, CCK-8 assay, scratch assay, and dual luciferase assay were used to carry out gene function identification. Through the analysis of the ceRNA networks, three miRNAs (miR-493-3p, miR-3959-3p, and miR-410-5p) and three genes (TEAD1, ZBTB34, and POGLUT1) were identified. The qRT-PCR of the DE-miRNAs and genes in the muscle tissues of sheep showed that the expression levels of the TEAD1 gene and miR-410-5p were correlated with the growth rate. The knockdown of the TEAD1 gene by siRNA could significantly inhibit the proliferation of sheep primary embryonic myoblasts, and the expression levels of SLC1A5, FoxO3, MyoD, and Pax7 were significantly downregulated. The targeting relationship between miR-410-5p and the TEAD1 gene was validated by a dual luciferase assay, and miR-410-5p can significantly downregulate the expression of TEAD1 in sheep primary embryonic myoblasts. We proved the regulatory relationship between miR-410-5p and the TEAD1 gene, which was related to the proliferation of sheep embryonic myoblasts. The results provide a reference and molecular basis for understanding the molecular mechanism of embryonic muscle development.

Small RNA Targets: Advances in Prediction Tools and High-Throughput Profiling

MicroRNAs (miRNAs) are an abundant class of small non-coding RNAs that regulate gene expression at the post-transcriptional level. They are suggested to be involved in most biological processes of the cell primarily by targeting messenger RNAs (mRNAs) for cleavage or translational repression. Their binding to their target sites is mediated by the Argonaute (AGO) family of proteins. Thus, miRNA target prediction is pivotal for research and clinical applications. Moreover, transfer-RNA-derived fragments (tRFs) and other types of small RNAs have been found to be potent regulators of Ago-mediated gene expression. Their role in mRNA regulation is still to be fully elucidated, and advancements in the computational prediction of their targets are in their infancy. To shed light on these complex RNA-RNA interactions, the availability of good quality high-throughput data and reliable computational methods is of utmost importance. Even though the arsenal of computational approaches in the field has been enriched in the last decade, there is still a degree of discrepancy between the results they yield. This review offers an overview of the relevant advancements in the field of bioinformatics and machine learning and summarizes the key strategies utilized for small RNA target prediction. Furthermore, we report the recent development of high-throughput sequencing technologies, and explore the role of non-miRNA AGO driver sequences.

miRNA/mRNA co-profiling identifies the miR-200 family as a central regulator of SMC quiescence

miRNAs are versatile regulators of smooth muscle cell (SMC) fate and behavior in vascular development and disease. Targeted loss-of-function studies have established the relevance of specific miRNAs in controlling SMC differentiation or mediating phenotypic modulation. Our goal was to characterize SMC miRNAome and its contribution to transcriptome changes during phenotypic modulation. Small RNA sequencing revealed that dedifferentiation led to the differential expression of over 50 miRNAs in cultured SMC. miRNA/mRNA comparison predicted that over a third of SMC transcript expression was regulated by differentially expressed miRNAs. Our screen identified the miR-200 cluster as highly downregulated during dedifferentiation. miR-200 maintains SMC quiescence and represses proliferation, migration, and neointima formation, in part by targeting Quaking, a central SMC phenotypic switching mediator. Our study unraveled the substantial contribution of miRNAs in regulating the SMC transcriptome and identified the miR-200 cluster as a pro-quiescence mechanism and a potential inhibitor of vascular restenosis.

MiRNA fine tuning for crop improvement: using advance computational models and biotechnological tools

MiRNAs modulate target genes expression at post-transcriptional levels, by reducing spatial abundance of mRNAs. MiRNAs regulats plant metabolism, and emerged as regulators of plant stress responses. Which make miRNAs promising candidates for fine tuning to affectively alter crop stress tolerance and other important traits. With recent advancements in the computational biology and biotechnology miRNAs structure and target prediction is possible resulting in pin point editing; miRNA modulation can be done by up or down regulating miRNAs using recently available biotechnological tools (CRISPR Cas9, TALENS and RNAi). In this review we have focused on miRNA biogenesis, miRNA roles in plant development, plant stress responses and roles in signaling pathways. Additionally we have discussed latest computational prediction models for miRNA to target gene interaction and biotechnological systems used recently for miRNA modulation. We have also highlighted setbacks and limitations in the way of miRNA modulation; providing entirely a new direction for improvement in plant genomics primarily focusing miRNAs.

TargetNet: functional microRNA target prediction with deep neural networks

MOTIVATION

MicroRNAs (miRNAs) play pivotal roles in gene expression regulation by binding to target sites of messenger RNAs (mRNAs). While identifying functional targets of miRNAs is of utmost importance, their prediction remains a great challenge. Previous computational algorithms have major limitations. They use conservative candidate target site (CTS) selection criteria mainly focusing on canonical site types, rely on laborious and time-consuming manual feature extraction, and do not fully capitalize on the information underlying miRNA-CTS interactions.

RESULTS

In this article, we introduce TargetNet, a novel deep learning-based algorithm for functional miRNA target prediction. To address the limitations of previous approaches, TargetNet has three key components: (i) relaxed CTS selection criteria accommodating irregularities in the seed region, (ii) a novel miRNA-CTS sequence encoding scheme incorporating extended seed region alignments and (iii) a deep residual network-based prediction model. The proposed model was trained with miRNA-CTS pair datasets and evaluated with miRNA-mRNA pair datasets. TargetNet advances the previous state-of-the-art algorithms used in functional miRNA target classification. Furthermore, it demonstrates great potential for distinguishing high-functional miRNA targets.

AVAILABILITY AND IMPLEMENTATION

The codes and pre-trained models are available at https://github.com/mswzeus/TargetNet.

Prediction methods for microRNA targets in bilaterian animals: Toward a better understanding by biologists

MicroRNAs (miRNAs) are small noncoding RNAs that regulate gene expression at the posttranscriptional level. Because of their wide network of interactions, miRNAs have become the focus of many studies over the past decade, particularly in animal species. To streamline the number of potential wet lab experiments, the use of miRNA target prediction tools is currently the first step undertaken. However, the predictions made may vary considerably depending on the tool used, which is mostly due to the complex and still not fully understood mechanism of action of miRNAs. The discrepancies complicate the choice of the tool for miRNA target prediction. To provide a comprehensive view of this issue, we highlight in this review the main characteristics of miRNA-target interactions in bilaterian animals, describe the prediction models currently used, and provide some insights for the evaluation of predictor performance.

Comprehensive machine-learning-based analysis of microRNA-target interactions reveals variable transferability of interaction rules across species

BACKGROUND

MicroRNAs (miRNAs) are small non-coding RNAs that regulate gene expression post-transcriptionally via base-pairing with complementary sequences on messenger RNAs (mRNAs). Due to the technical challenges involved in the application of high-throughput experimental methods, datasets of direct bona fide miRNA targets exist only for a few model organisms. Machine learning (ML)-based target prediction models were successfully trained and tested on some of these datasets. There is a need to further apply the trained models to organisms in which experimental training data are unavailable. However, it is largely unknown how the features of miRNA-target interactions evolve and whether some features have remained fixed during evolution, raising questions regarding the general, cross-species applicability of currently available ML methods.

RESULTS

We examined the evolution of miRNA-target interaction rules and used data science and ML approaches to investigate whether these rules are transferable between species. We analyzed eight datasets of direct miRNA-target interactions in four species (human, mouse, worm, cattle). Using ML classifiers, we achieved high accuracy for intra-dataset classification and found that the most influential features of all datasets overlap significantly. To explore the relationships between datasets, we measured the divergence of their miRNA seed sequences and evaluated the performance of cross-dataset classification. We found that both measures coincide with the evolutionary distance between the compared species.

CONCLUSIONS

The transferability of miRNA-targeting rules between species depends on several factors, the most associated factors being the composition of seed families and evolutionary distance. Furthermore, our feature-importance results suggest that some miRNA-target features have evolved while others remained fixed during the evolution of the species. Our findings lay the foundation for the future development of target prediction tools that could be applied to "non-model" organisms for which minimal experimental data are available.

AVAILABILITY AND IMPLEMENTATION

The code is freely available at https://github.com/gbenor/TPVOD .

Simultaneous learning of individual microRNA-gene interactions and regulatory comodules

BACKGROUND

MicroRNAs (miRNAs) function in post-transcriptional regulation of gene expression by binding to target messenger RNAs (mRNAs). Because of the key part that miRNAs play, understanding the correct regulatory role of miRNAs in diverse patho-physiological conditions is of great interest. Although it is known that miRNAs act combinatorially to regulate genes, precise identification of miRNA-gene interactions and their specific functional roles in regulatory comodules remains a challenge. We developed THEIA, an effective method for simultaneously predicting miRNA-gene interactions and regulatory comodules, which group functionally related miRNAs and genes via non-negative matrix factorization (NMF).

RESULTS

We apply THEIA to RNA sequencing data from breast invasive carcinoma samples and demonstrate its effectiveness in discovering biologically significant regulatory comodules that are significantly enriched in spatial miRNA clusters, biological pathways, and various cancers.

CONCLUSIONS

THEIA is a theoretically rigorous optimization algorithm that simultaneously predicts the strength and direction (i.e., up-regulation or down-regulation) of the effect of modules of miRNAs on a gene. We posit that if THEIA is capable of recovering known clusters of genes and miRNA, then the clusters found by our method not previously identified by literature are also likely to have biological significance. We believe that these novel regulatory comodules found by our method will be a springboard for further research into the specific functional roles of these new functional ensembles of miRNAs and genes,especially those related to diseases like breast cancer.

microRNA-31 regulates skeletogenesis by direct suppression of Eve and Wnt1

microRNAs (miRNAs) play a critical role in a variety of biological processes, including embryogenesis and the physiological functions of cells. Evolutionarily conserved microRNA-31 (miR-31) has been found to be involved in cancer, bone formation, and lymphatic development. We previously discovered that, in the sea urchin, miR-31 knockdown (KD) embryos have shortened dorsoventral connecting rods, mispatterned skeletogenic primary mesenchyme cells (PMCs) and shifted and expanded Vegf3 expression domain. Vegf3 itself does not contain miR-31 binding sites; however, we identified its upstream regulators Eve and Wnt1 to be directly suppressed by miR-31. Removal of miR-31's suppression of Eve and Wnt1 resulted in skeletal and PMC patterning defects, similar to miR-31 KD phenotypes. Additionally, removal of miR-31's suppression of Eve and Wnt1 results in an expansion and anterior shift in expression of Veg1 ectodermal genes, including Vegf3 in the blastulae. This indicates that miR-31 indirectly regulates Vegf3 expression through directly suppressing Eve and Wnt1. Furthermore, removing miR-31 suppression of Eve is sufficient to cause skeletogenic defects, revealing a novel regulatory role of Eve in skeletogenesis and PMC patterning. Overall, this study provides a proposed molecular mechanism of miR-31's regulation of skeletogenesis and PMC patterning through its cross-regulation of a Wnt signaling ligand and a transcription factor of the endodermal and ectodermal gene regulatory network.

MiR-142-5p Suppresses Lung Cancer Cell Metastasis by Targeting Yin Yang 1 to Regulate Epithelial-Mesenchymal Transition

This study aimed to investigate the mechanism of miR-142-5p and Yin Yang 1 (YY1) on regulating epithelial-mesenchymal transition (EMT) in lung cancer cell metastasis. The expressions of YY1 and miR-142-5p in different lung cancer cell lines were negatively correlated. The results of the dual-luciferase reporter assay further validated that miR-142-5p directly targeted YY1. Subsequently, transwell assays, wound-healing assay, and transplantation tumor model in nude mice proved that YY1 could promote the metastasis of lung cancer cells, whereas miR-142-5p impaired the stimulating effect of YY1 on the metastasis ability of lung cancer cells in vitro and in vivo. Western blot and quantitative real-time polymerase chain reaction analysis of the EMT-related proteins indicated that YY1 could enhance the metastasis ability of lung cancer cells by promoting EMT. On the contrary, miR-142-5p constrained the expression of mesenchymal markers by targeting YY1, reversed the differentiation of cells into mesenchymal cells, and weakened the metastasis ability of tumor cells in vitro and in vivo. In summary, miR-142-5p may regulate the expressions of EMT-related proteins by targeting YY1, thereby inhibiting lung cancer metastasis, which provides a promising therapeutic target for lung cancer.

Computational prediction of miRNA/mRNA duplexomes at the whole human genome scale reveals functional subnetworks of interacting genes with embedded miRNA annealing motifs

Perfect annealing between microRNAs (miRNAs) and messenger RNAs (mRNAs) was computationally searched at a broad scale in the human genome to determine whether theoretical pairing is restrictively represented in functional subnetworks or is randomly distributed. Massive RNA interference (RNAi) pairing motifs in genes constitute a remarkable subnetwork that displays highly genetically and biochemically interconnected genes. These analyses show unexpected repertoires of genes defined by their congruence in comatching with miRNAs at numerous sites and by their interconnection based on protein/protein interactions or proteins regulating the activity of others. This offers insights into the putatively coregulated homeostasis of large networks of genes by RNAi, whereas other networks seem to be independent of this regulatory mode. Genes accordingly defined by theoretical RNAi pairing cluster mainly in subnetworks related to cellular, metabolic and developmental processes and their regulation. Indeed, genes harboring numerous potential sites of hybridization with miRNAs are highly enriched with GO terms depicting the abovementioned processes and are grouped in a subnetwork of genes that are significantly more highly connected than they would be according to a random distribution. The significant number of interacting genes that present numerous potential comatches with miRNAs suggests that they may be under the control of the integrative and concerted action of multiple miRNAs.

Computational discovery and modeling of novel gene expression rules encoded in the mRNA

The transcript is populated with numerous overlapping codes that regulate all steps of gene expression. Deciphering these codes is very challenging due to the large number of variables involved, the non-modular nature of the codes, biases and limitations in current experimental approaches, our limited knowledge in gene expression regulation across the tree of life, and other factors. In recent years, it has been shown that computational modeling and algorithms can significantly accelerate the discovery of novel gene expression codes. Here, we briefly summarize the latest developments and different approaches in the field.

Population variation in miRNAs and isomiRs and their impact on human immunity to infection

BACKGROUND

MicroRNAs (miRNAs) are key regulators of the immune system, yet their variation and contribution to intra- and inter-population differences in immune responses is poorly characterized.

RESULTS

We generate 977 miRNA-sequencing profiles from primary monocytes from individuals of African and European ancestry following activation of three TLR pathways (TLR4, TLR1/2, and TLR7/8) or infection with influenza A virus. We find that immune activation leads to important modifications in the miRNA and isomiR repertoire, particularly in response to viral challenges. These changes are much weaker than those observed for protein-coding genes, suggesting stronger selective constraints on the miRNA response to stimulation. This is supported by the limited genetic control of miRNA expression variability (miR-QTLs) and the lower occurrence of gene-environment interactions, in stark contrast with eQTLs that are largely context-dependent. We also detect marked differences in miRNA expression between populations, which are mostly driven by non-genetic factors. On average, miR-QTLs explain approximately 60% of population differences in expression of their cognate miRNAs and, in some cases, evolve adaptively, as shown in Europeans for a miRNA-rich cluster on chromosome 14. Finally, integrating miRNA and mRNA data from the same individuals, we provide evidence that the canonical model of miRNA-driven transcript degradation has a minor impact on miRNA-mRNA correlations, which are, in our setting, mainly driven by co-transcription.

CONCLUSION

Together, our results shed new light onto the factors driving miRNA and isomiR diversity at the population level and constitute a useful resource for evaluating their role in host differences of immunity to infection.

Production and Purification of Artificial Circular RNA Sponges for Application in Molecular Biology and Medicine

Characterized by their covalently closed structure and thus an elevated stability compared to linear RNA molecules, circular RNAs (circRNAs) form a novel class of mainly non-coding RNAs. Although the biological functions of naturally occurring circRNAs are largely unknown, they were reported to act as molecular sponges, sequestering microRNAs (miRNAs), resulting in a de-repression of target mRNAs. Taking these characteristics of naturally occurring circRNAs into account, artificial circRNAs could be a potential tool in molecular biology and medicine. Using the Hepatitis C virus (HCV) as a model system, this application of artificial circular RNAs was demonstrated. The virus requires cellular miRNA miR-122 for its life cycle, and circRNAs specifically engineered to efficiently sequester this miRNA impacted viral propagation. Since in this context the production of engineered circRNA remains the limiting factor, we present a method to produce and efficiently purify artificial circRNA sponges (ciRS) in vitro. In this protocol we provide insights into a small-scale and large-scale production technique of artificial circular RNA sponges relying on in vitro transcription and RNA ligation.

FilTar: using RNA-Seq data to improve microRNA target prediction accuracy in animals

MOTIVATION

MicroRNA (miRNA) target prediction algorithms do not generally consider biological context and therefore generic target prediction based on seed binding can lead to a high level of false-positive predictions. Here, we present FilTar, a method that incorporates RNA-Seq data to make miRNA target prediction specific to a given cell type or tissue of interest.

RESULTS

We demonstrate that FilTar can be used to: (i) provide sample specific 3'-UTR reannotation; extending or truncating default annotations based on RNA-Seq read evidence and (ii) filter putative miRNA target predictions by transcript expression level, thus removing putative interactions where the target transcript is not expressed in the tissue or cell line of interest. We test the method on a variety of miRNA transfection datasets and demonstrate increased accuracy versus generic miRNA target prediction methods.

AVAILABILITY AND IMPLEMENTATION

FilTar is freely available and can be downloaded from https://github.com/TBradley27/FilTar. The tool is implemented using the Python and R programming languages, and is supported on GNU/Linux operating systems.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

microRNA-mediated noise processing in cells: A fight or a game?

In the past decades, microRNAs (miRNA) have much attracted the attention of researchers at the interface between life and theoretical sciences for their involvement in post-transcriptional regulation and related diseases. Thanks to the always more sophisticated experimental techniques, the role of miRNAs as "noise processing units" has been further elucidated and two main ways of miRNA noise-control have emerged by combinations of theoretical and experimental studies. While on one side miRNAs were thought to buffer gene expression noise, it has recently been suggested that miRNAs could also increase the cell-to-cell variability of their targets. In this Mini Review, we focus on the role of miRNAs in molecular noise processing and on the advantages as well as current limitations of theoretical modelling.

The biochemical basis of microRNA targeting efficacy

MicroRNAs (miRNAs) act within Argonaute proteins to guide repression of messenger RNA targets. Although various approaches have provided insight into target recognition, the sparsity of miRNA-target affinity measurements has limited understanding and prediction of targeting efficacy. Here, we adapted RNA bind-n-seq to enable measurement of relative binding affinities between Argonaute-miRNA complexes and all sequences ≤12 nucleotides in length. This approach revealed noncanonical target sites specific to each miRNA, miRNA-specific differences in canonical target-site affinities, and a 100-fold impact of dinucleotides flanking each site. These data enabled construction of a biochemical model of miRNA-mediated repression, which was extended to all miRNA sequences using a convolutional neural network. This model substantially improved prediction of cellular repression, thereby providing a biochemical basis for quantitatively integrating miRNAs into gene-regulatory networks.

High-Throughput Analysis Reveals Rules for Target RNA Binding and Cleavage by AGO2

Argonaute proteins loaded with microRNAs (miRNAs) or small interfering RNAs (siRNAs) form the RNA-induced silencing complex (RISC), which represses target RNA expression. Predicting the biological targets, specificity, and efficiency of both miRNAs and siRNAs has been hamstrung by an incomplete understanding of the sequence determinants of RISC binding and cleavage. We applied high-throughput methods to measure the association kinetics, equilibrium binding energies, and single-turnover cleavage rates of mouse AGO2 RISC. We find that RISC readily tolerates insertions of up to 7 nt in its target opposite the central region of the guide. Our data uncover specific guide:target mismatches that enhance the rate of target cleavage, suggesting novel siRNA design strategies. Using these data, we derive quantitative models for RISC binding and target cleavage and show that our in vitro measurements and models predict knockdown in an engineered cellular system.

Computational analysis of ribonomics datasets identifies long non-coding RNA targets of γ-herpesviral miRNAs

Ribonomics experiments involving crosslinking and immuno-precipitation (CLIP) of Ago proteins have expanded the understanding of the miRNA targetome of several organisms. These techniques, collectively referred to as CLIP-seq, have been applied to identifying the mRNA targets of miRNAs expressed by Kaposi’s Sarcoma-associated herpes virus (KSHV) and Epstein–Barr virus (EBV). However, these studies focused on identifying only those RNA targets of KSHV and EBV miRNAs that are known to encode proteins. Recent studies have demonstrated that long non-coding RNAs (lncRNAs) are also targeted by miRNAs. In this study, we performed a systematic re-analysis of published datasets from KSHV- and EBV-driven cancers. We used CLIP-seq data from lymphoma cells or EBV-transformed B cells, and a crosslinking, ligation and sequencing of hybrids dataset from KSHV-infected endothelial cells, to identify novel lncRNA targets of viral miRNAs. Here, we catalog the lncRNA targetome of KSHV and EBV miRNAs, and provide a detailed in silico analysis of lncRNA–miRNA binding interactions. Viral miRNAs target several hundred lncRNAs, including a subset previously shown to be aberrantly expressed in human malignancies. In addition, we identified thousands of lncRNAs to be putative targets of human miRNAs, suggesting that miRNA–lncRNA interactions broadly contribute to the regulation of gene expression.

Recent computational developments on CLIP-seq data analysis and microRNA targeting implications

Cross-Linking Immunoprecipitation associated to high-throughput sequencing (CLIP-seq) is a technique used to identify RNA directly bound to RNA-binding proteins across the entire transcriptome in cell or tissue samples. Recent technological and computational advances permit the analysis of many CLIP-seq samples simultaneously, allowing us to reveal the comprehensive network of RNA–protein interaction and to integrate it to other genome-wide analyses. Therefore, the design and quality management of the CLIP-seq analyses are of critical importance to extract clean and biological meaningful information from CLIP-seq experiments. The application of CLIP-seq technique to Argonaute 2 (Ago2) protein, the main component of the microRNA (miRNA)-induced silencing complex, reveals the direct binding sites of miRNAs, thus providing insightful information about the role played by miRNA(s). In this review, we summarize and discuss the most recent computational methods for CLIP-seq analysis, and discuss their impact on Ago2/miRNA-binding site identification and prediction with a regard toward human pathologies.

Hybridization Kinetics Explains CRISPR-Cas Off-Targeting Rules

Due to their specificity, efficiency, and ease of programming, CRISPR-associated nucleases are popular tools for genome editing. On the genomic scale, these nucleases still show considerable off-target activity though, posing a serious obstacle to the development of therapies. Off targeting is often minimized by choosing especially high-specificity guide sequences, based on algorithms that codify empirically determined off-targeting rules. A lack of mechanistic understanding of these rules has so far necessitated their ad hoc implementation, likely contributing to the limited precision of present algorithms. To understand the targeting rules, we kinetically model the physics of guide-target hybrid formation. Using only four parameters, our model elucidates the kinetic origin of the experimentally observed off-targeting rules, thereby rationalizing the results from both binding and cleavage assays. We favorably compare our model to published data from CRISPR-Cas9, CRISPR-Cpf1, CRISPR-Cascade, as well as the human Argonaute 2 system.

Effects of the PIWI/MID domain of Argonaute protein on the association of miRNAi's seed base with the target

The small interfering RNAs (siRNA) or microRNAs (miRNA) incorporated into the RNA-induced silencing complex with the Argonaute (Ago) protein associates with target mRNAs through base-pairing, which leads to the cleavage or knockdown of the target mRNA. The seed region of the s(m)iRNA is crucial for target recognition. In this work, a molecular dynamic simulation was utilized to study the thermodynamics and kinetic properties of the third seed base binding to the target in the presence of the PIWI/MID domain of Ago. The results showed that in the presence of the PIWI/MID domain, the entropy and enthalpy changes for the association of the seed base with the target were smaller than those in the absence of protein. The binding affinity was increased due to the reduced entropy penalty, which resulted from the preorganization of the seed base into the A-helix form. In the presence of the protein, the association barrier resulting from the unfavorable entropy loss and the dissociation barrier coming from the destruction of hydrogen bonding and base-stacking interactions were lower than those in the absence of the protein. These results indicate that the seed region is crucial for fast recognition and association with the correct target.

Global identification of functional microRNA-mRNA interactions in Drosophila

MicroRNAs (miRNAs) are key mediators of post-transcriptional gene expression silencing. So far, no comprehensive experimental annotation of functional miRNA target sites exists in Drosophila. Here, we generated a transcriptome-wide in vivo map of miRNA-mRNA interactions in Drosophila melanogaster, making use of single nucleotide resolution in Argonaute1 (AGO1) crosslinking and immunoprecipitation (CLIP) data. Absolute quantification of cellular miRNA levels presents the miRNA pool in Drosophila cell lines to be more diverse than previously reported. Benchmarking two CLIP approaches, we identify a similar predictive potential to unambiguously assign thousands of miRNA-mRNA pairs from AGO1 interaction data at unprecedented depth, achieving higher signal-to-noise ratios than with computational methods alone. Quantitative RNA-seq and sub-codon resolution ribosomal footprinting data upon AGO1 depletion enabled the determination of miRNA-mediated effects on target expression and translation. We thus provide the first comprehensive resource of miRNA target sites and their quantitative functional impact in Drosophila.

The AGO proteins: an overview

Small RNAs govern almost every biological process in eukaryotes associating with the Argonaute (AGO) proteins to form the RNA-induced silencing complex (mRISC). AGO proteins constitute the core of RISCs with different members having variety of protein-binding partners and biochemical properties. This review focuses on the AGO subfamily of the AGOs that are ubiquitously expressed and are associated with small RNAs. The structure, function and role of the AGO proteins in the cell is discussed in detail.

Prediction of functional microRNA targets by integrative modeling of microRNA binding and target expression data

We perform a large-scale RNA sequencing study to experimentally identify genes that are downregulated by 25 miRNAs. This RNA-seq dataset is combined with public miRNA target binding data to systematically identify miRNA targeting features that are characteristic of both miRNA binding and target downregulation. By integrating these common features in a machine learning framework, we develop and validate an improved computational model for genome-wide miRNA target prediction. All prediction data can be accessed at miRDB ( http://mirdb.org ).

Modulation of Bacterial sRNAs Activity by Epigenetic Modifications: Inputs from the Eukaryotic miRNAs

Trans-encoded bacterial regulatory RNAs (sRNAs) are functional analogues of eukaryotic microRNAs (miRNAs). These RNA classes act by base-pairing complementarity with their RNA targets to modulate gene expression (transcription, half-life and/or translation). Based on base-pairing, algorithms predict binding and the impact of small RNAs on targeted-RNAs expression and fate. However, other actors are involved such as RNA binding proteins and epigenetic modifications of the targeted and small RNAs. Post-transcriptional base modifications are widespread in all living organisms where they lower undesired RNA folds through conformation adjustments and influence RNA pairing and stability, especially if remodeling their ends. In bacteria, sRNAs possess RNA modifications either internally (methylation, pseudouridinylation) or at their ends. Nicotinamide adenine dinucleotide were detected at 5′-ends, and polyadenylation can occur at 3′-ends. Eukaryotic miRNAs possess N⁶-methyladenosine (m⁶A), A editing into I, and non-templated addition of uridines at their 3′-ends. Biological functions and enzymes involved in those sRNA and micro RNA epigenetic modifications, when known, are presented and challenged.

Predicting microRNA targeting efficacy in Drosophila

Background

MicroRNAs (miRNAs) are short regulatory RNAs that derive from hairpin precursors. Important for understanding the functional roles of miRNAs is the ability to predict the messenger RNA (mRNA) targets most responsive to each miRNA. Progress towards developing quantitative models of miRNA targeting in Drosophila and other invertebrate species has lagged behind that of mammals due to the paucity of datasets measuring the effects of miRNAs on mRNA levels.

Results

We acquired datasets suitable for the quantitative study of miRNA targeting in Drosophila. Analyses of these data expanded the types of regulatory sites known to be effective in flies, expanded the mRNA regions with detectable targeting to include 5′ untranslated regions, and identified features of site context that correlate with targeting efficacy in fly cells. Updated evolutionary analyses evaluated the probability of conserved targeting for each predicted site and indicated that more than a third of the Drosophila genes are preferentially conserved targets of miRNAs. Based on these results, a quantitative model was developed to predict targeting efficacy in insects. This model performed better than existing models, and it drives the most recent version, v7, of TargetScanFly.

Conclusions

Our evolutionary and functional analyses expand the known scope of miRNA targeting in flies and other insects. The existence of a quantitative model that has been developed and trained using Drosophila data will provide a valuable resource for placing miRNAs into gene regulatory networks of this important experimental organism.

Electronic supplementary material

The online version of this article (10.1186/s13059-018-1504-3) contains supplementary material, which is available to authorized users.

microCLIP super learning framework uncovers functional transcriptome-wide miRNA interactions

Argonaute crosslinking and immunoprecipitation (CLIP) experiments are the most widely used high-throughput methodologies for miRNA targetome characterization. The analysis of Photoactivatable Ribonucleoside-Enhanced (PAR) CLIP methodology focuses on sequence clusters containing T-to-C conversions. Here, we demonstrate for the first time that the non-T-to-C clusters, frequently observed in PAR-CLIP experiments, exhibit functional miRNA-binding events and strong RNA accessibility. This discovery is based on the analysis of an extensive compendium of bona fide miRNA-binding events, and is further supported by numerous miRNA perturbation experiments and structural sequencing data. The incorporation of these previously neglected clusters yields an average of 14% increase in miRNA-target interactions per PAR-CLIP library. Our findings are integrated in microCLIP (www.microrna.gr/microCLIP), a cutting-edge framework that combines deep learning classifiers under a super learning scheme. The increased performance of microCLIP in CLIP-Seq-guided detection of miRNA interactions, uncovers previously elusive regulatory events and miRNA-controlled pathways.

AGO-PAR-CLIP is widely used for high-throughput miRNA target characterization. Here, the authors show that the previously neglected non-T-to-C clusters denote functional miRNA binding events, and develop microCLIP, a super learning framework that accurately detects miRNA interactions.

Mapping transcriptome-wide protein-RNA interactions to elucidate RNA regulatory programs

BACKGROUND

Our understanding of post-transcriptional gene regulation has increased exponentially with the development of robust methods to define protein-RNA interactions across the transcriptome. In this review, we highlight the evolution and successful applications of crosslinking and immunoprecipitation (CLIP) methods to interrogate protein-RNA interactions in a transcriptome-wide manner.

RESULTS

Here, we survey the vast array of in vitro and in vivo approaches used to identify protein-RNA interactions, including but not limited to electrophoretic mobility shift assays, systematic evolution of ligands by exponential enrichment (SELEX), and RIP-seq. We particularly emphasize the advancement of CLIP technologies, and detail protocol improvements and computational tools used to analyze the output data. Importantly, we discuss how profiling protein-RNA interactions can delineate biological functions including splicing regulation, alternative polyadenylation, cytoplasmic decay substrates, and miRNA targets.

CONCLUSIONS

In summary, this review summarizes the benefits of characterizing RNA-protein networks to further understand the regulation of gene expression and disease pathogenesis. Our review comments on how future CLIP technologies can be adapted to address outstanding questions related to many aspects of RNA metabolism and further advance our understanding of RNA biology.

Rules for functional microRNA targeting

MicroRNAs (miRNAs) are ~22nt-long single-stranded RNA molecules that form a RNA-induced silencing complex with Argonaute (AGO) protein to post-transcriptionally downregulate their target messenger RNAs (mRNAs). To understand the regulatory mechanisms of miRNA, discovering the underlying functional rules for how miRNAs recognize and repress their target mRNAs is of utmost importance. To determine functional miRNA targeting rules, previous studies extensively utilized various methods including high-throughput biochemical assays and bioinformatics analyses. However, targeting rules reported in one study often fail to be reproduced in other studies and therefore the general rules for functional miRNA targeting remain elusive. In this review, we evaluate previously-reported miRNA targeting rules and discuss the biological impact of the functional miRNAs on gene-regulatory networks as well as the future direction of miRNA targeting research.

A Distributed Classifier for MicroRNA Target Prediction with Validation Through TCGA Expression Data

BACKGROUND

MicroRNAs (miRNAs) are approximately 22-nucleotide long regulatory RNA that mediate RNA interference by binding to cognate mRNA target regions. Here, we present a distributed kernel SVM-based binary classification scheme to predict miRNA targets. It captures the spatial profile of miRNA-mRNA interactions via smooth B-spline curves. This is accomplished separately for various input features, such as thermodynamic and sequence-based features. Further, we use a principled approach to uniformly model both canonical and non-canonical seed matches, using a novel seed enrichment metric. Finally, we verify our miRNA-mRNA pairings using an Elastic Net-based regression model on TCGA expression data for four cancer types to estimate the miRNAs that together regulate any given mRNA.

RESULTS

We present a suite of algorithms for miRNA target prediction, under the banner Avishkar, with superior prediction performance over the competition. Specifically, our final kernel SVM model, with an Apache Spark backend, achieves an average true positive rate (TPR) of more than 75 percent, when keeping the false positive rate of 20 percent, for non-canonical human miRNA target sites. This is an improvement of over 150 percent in the TPR for non-canonical sites, over the best-in-class algorithm. We are able to achieve such superior performance by representing the thermodynamic and sequence profiles of miRNA-mRNA interaction as curves, devising a novel seed enrichment metric, and learning an ensemble of miRNA family-specific kernel SVM classifiers. We provide an easy-to-use system for large-scale interactive analysis and prediction of miRNA targets. All operations in our system, namely candidate set generation, feature generation and transformation, training, prediction, and computing performance metrics are fully distributed and are scalable.

CONCLUSIONS

We have developed an efficient SVM-based model for miRNA target prediction using recent CLIP-seq data, demonstrating superior performance, evaluated using ROC curves for different species (human or mouse), or different target types (canonical or non-canonical). We analyzed the agreement between the target pairings using CLIP-seq data and using expression data from four cancer types. To the best of our knowledge, we provide the first distributed framework for miRNA target prediction based on Apache Hadoop and Spark.

AVAILABILITY

All source code and sample data are publicly available at https://bitbucket.org/cellsandmachines/avishkar. Our scalable implementation of kernel SVM using Apache Spark, which can be used to solve large-scale non-linear binary classification problems, is available at https://bitbucket.org/cellsandmachines/kernelsvmspark.

Single-Molecule View of Small RNA-Guided Target Search and Recognition

Most everyday processes in life involve a necessity for an entity to locate its target. On a cellular level, many proteins have to find their target to perform their function. From gene-expression regulation to DNA repair to host defense, numerous nucleic acid-interacting proteins use distinct target search mechanisms. Several proteins achieve that with the help of short RNA strands known as guides. This review focuses on single-molecule advances studying the target search and recognition mechanism of Argonaute and CRISPR (clustered regularly interspaced short palindromic repeats) systems. We discuss different steps involved in search and recognition, from the initial complex prearrangement into the target-search competent state to the final proofreading steps. We focus on target search mechanisms that range from weak interactions, to one- and three-dimensional diffusion, to conformational proofreading. We compare the mechanisms of Argonaute and CRISPR with a well-studied target search system, RecA.

Modified Cross-Linking, Ligation, and Sequencing of Hybrids (qCLASH) Identifies Kaposi's Sarcoma-Associated Herpesvirus MicroRNA Targets in Endothelial Cells

Kaposi's sarcoma (KS) tumors are derived from endothelial cells and express Kaposi's sarcoma-associated herpesvirus (KSHV) microRNAs (miRNAs). Although miRNA targets have been identified in B cell lymphoma-derived cells and epithelial cells, little has been done to characterize the KSHV miRNA targetome in endothelial cells. A recent innovation in the identification of miRNA targetomes, cross-linking, ligation, and sequencing of hybrids (CLASH), unambiguously identifies miRNAs and their targets by ligating the two species while both species are still bound within the RNA-induced silencing complex (RISC). We developed a streamlined quick CLASH (qCLASH) protocol that requires a lower cell input than the original method and therefore has the potential to be used on patient biopsy samples. Additionally, we developed a fast-growing, KSHV-negative endothelial cell line derived from telomerase-immortalized vein endothelial long-term culture (TIVE-LTC) cells. qCLASH was performed on uninfected cells and cells infected with either wild-type KSHV or a mutant virus lacking miR-K12-11/11*. More than 1,400 cellular targets of KSHV miRNAs were identified. Many of the targets identified by qCLASH lacked a canonical seed sequence match. Additionally, most target regions in mRNAs originated from the coding DNA sequence (CDS) rather than the 3' untranslated region (UTR). This set of genes includes some that were previously identified in B cells and some new genes that warrant further study. Pathway analysis of endothelial cell targets showed enrichment in cell cycle control, apoptosis, and glycolysis pathways, among others. Characterization of these new targets and the functional consequences of their repression will be important in furthering our understanding of the role of KSHV miRNAs in oncogenesis.IMPORTANCE KS lesions consist of endothelial cells latently infected with KSHV. Cells that make up these lesions express KSHV miRNAs. Identification of the targets of KSHV miRNAs will help us understand their role in viral oncogenesis. The cross-linking and sequencing of hybrids (CLASH) protocol is a method for unambiguously identifying miRNA targetomes. We developed a streamlined version of CLASH, called quick CLASH (qCLASH). qCLASH requires a lower initial input of cells than for its parent protocol. Additionally, a new fast-growing KSHV-negative endothelial cell line, named TIVE-EX-LTC cells, was established. qCLASH was performed on TIVE-EX-LTC cells latently infected with wild-type (WT) KSHV or a mutant virus lacking miR-K12-11/11*. A number of novel targets of KSHV miRNAs were identified, including targets of miR-K12-11, the ortholog of the cellular oncogenic miRNA (oncomiR) miR-155. Many of the miRNA targets were involved in processes related to oncogenesis, such as glycolysis, apoptosis, and cell cycle control.

Tiresias: Context-sensitive Approach to Decipher the Presence and Strength of MicroRNA Regulatory Interactions

MicroRNAs (miRNAs) are short non-coding RNAs that regulate expression of target messenger RNAs (mRNAs) post-transcriptionally. Understanding the precise regulatory role of miRNAs is of great interest since miRNAs have been shown to play an important role in development, diseases, and other biological processes. Early work on miRNA target prediction has focused on static sequence-driven miRNA-mRNA complementarity. However, recent research also utilizes expression-level data to study context-dependent regulation effects in a more dynamic, physiologically-relevant setting. Methods: We propose a novel artificial neural network (ANN) based method, named Tiresias, to predict such targets in a context-dependent manner by combining sequence and expression data. In order to predict the interacting pairs among miRNAs and mRNAs and their regulatory weights, we develop a two-stage ANN and present how to train it appropriately. Tiresias is designed to study various regulation models, ranging from a simple linear model to a complex non-linear model. Tiresias has a single hyper-parameter to control the sparsity of miRNA-mRNA interactions, which we optimize using Bayesian optimization. Results: Tiresias performs better than existing computational methods such as GenMiR++, Elastic Net, and PIMiM, achieving an F1 score of >0.8 for a certain level of regulation strength. For the TCGA breast invasive carcinoma dataset, Tiresias results in the rate of up to 82% in detecting the experimentally-validated interactions between miRNAs and mRNAs, even if we assume that true regulations may result in a low level of regulation strength. Conclusion: Tiresias is a two-stage ANN, computational method that deciphers context-dependent microRNA regulatory interactions. Experiment results demonstrate that Tiresias outperforms existing solutions and can achieve a high F1 score. Source code of Tiresias is available at https://bitbucket.org/cellsandmachines/.

A comprehensive, cell specific microRNA catalogue of human peripheral blood

With this study, we provide a comprehensive reference dataset of detailed miRNA expression profiles from seven types of human peripheral blood cells (NK cells, B lymphocytes, cytotoxic T lymphocytes, T helper cells, monocytes, neutrophils and erythrocytes), serum, exosomes and whole blood. The peripheral blood cells from buffy coats were typed and sorted using FACS/MACS. The overall dataset was generated from 450 small RNA libraries using high-throughput sequencing. By employing a comprehensive bioinformatics and statistical analysis, we show that 3′ trimming modifications as well as composition of 3′ added non-templated nucleotides are distributed in a lineage-specific manner—the closer the hematopoietic progenitors are, the higher their similarities in sequence variation of the 3′ end. Furthermore, we define the blood cell-specific miRNA and isomiR expression patterns and identify novel cell type specific miRNA candidates. The study provides the most comprehensive contribution to date towards a complete miRNA catalogue of human peripheral blood, which can be used as a reference for future studies. The dataset has been deposited in GEO and also can be explored interactively following this link: http://134.245.63.235/ikmb-tools/bloodmiRs.

STarMirDB: A database of microRNA binding sites

microRNAs (miRNAs) are an abundant class of small endogenous non-coding RNAs (ncRNAs) of ∼22 nucleotides (nts) in length. These small regulatory molecules are involved in diverse developmental, physiological and pathological processes. miRNAs target mRNAs (mRNAs) for translational repression and/or mRNA degradation. Predictions of miRNA binding sites facilitate experimental validation of miRNA targets. Models developed with data from CLIP studies have been used for predictions of miRNA binding sites in the whole transcriptomes of human, mouse and worm. The prediction results have been assembled into STarMirDB, a new database of miRNA binding sites available at http://sfold.wadsworth.org/starmirDB.php. STarMirDB can be searched by miRNAs or mRNAs separately or in combination. The search results are categorized into seed and seedless sites in 3′ UTR, CDS and 5′ UTR. For each predicted site, STarMirDB provides a comprehensive list of sequence, thermodynamic and target structural features that are known to influence miRNA: target interaction. A high resolution PDF diagram of the conformation of the miRNA:target hybrid is also available for visualization and publication. The results of a database search are available through both an interactive viewer and downloadable text files.

A non-coding function of TYRP1 mRNA promotes melanoma growth

Competition among RNAs to bind miRNA is proposed to influence biological systems. However, the role of this competition in disease onset is unclear. Here, we report that TYRP1 mRNA, in addition to encoding tyrosinase-related protein 1 (TYRP1), indirectly promotes cell proliferation by sequestering miR-16 on non-canonical miRNA response elements. Consequently, the sequestered miR-16 is no longer able to repress its mRNA targets, such as RAB17, which is involved in melanoma cell proliferation and tumour growth. Restoration of miR-16 tumour-suppressor function can be achieved in vitro by silencing TYRP1 or increasing miR-16 expression. Importantly, TYRP1-dependent miR-16 sequestration can also be overcome in vivo by using small oligonucleotides that mask miR-16-binding sites on TYRP1 mRNA. Together, our findings assign a pathogenic non-coding function to TYRP1 mRNA and highlight miRNA displacement as a promising targeted therapeutic approach for melanoma.

From benchmarking HITS-CLIP peak detection programs to a new method for identification of miRNA-binding sites from Ago2-CLIP data

Experimental evidence indicates that about 60% of miRNA-binding activity does not follow the canonical rule about the seed matching between miRNA and target mRNAs, but rather a non-canonical miRNA targeting activity outside the seed or with a seed-like motifs. Here, we propose a new unbiased method to identify canonical and non-canonical miRNA-binding sites from peaks identified by Ago2 Cross-Linked ImmunoPrecipitation associated to high-throughput sequencing (CLIP-seq). Since the quality of peaks is of pivotal importance for the final output of the proposed method, we provide a comprehensive benchmarking of four peak detection programs, namely CIMS, PIPE-CLIP, Piranha and Pyicoclip, on four publicly available Ago2-HITS-CLIP datasets and one unpublished in-house Ago2-dataset in stem cells. We measured the sensitivity, the specificity and the position accuracy toward miRNA binding sites identification, and the agreement with TargetScan. Secondly, we developed a new pipeline, called miRBShunter, to identify canonical and non-canonical miRNA-binding sites based on de novo motif identification from Ago2 peaks and prediction of miRNA::RNA heteroduplexes. miRBShunter was tested and experimentally validated on the in-house Ago2-dataset and on an Ago2-PAR-CLIP dataset in human stem cells. Overall, we provide guidelines to choose a suitable peak detection program and a new method for miRNA-target identification.

MicroTarget: MicroRNA target gene prediction approach with application to breast cancer

MicroRNAs are known to play an essential role in gene regulation in plants and animals. The standard method for understanding microRNA–gene interactions is randomized controlled perturbation experiments. These experiments are costly and time consuming. Therefore, use of computational methods is essential. Currently, several computational methods have been developed to discover microRNA target genes. However, these methods have limitations based on the features that are used for prediction. The commonly used features are complementarity to the seed region of the microRNA, site accessibility, and evolutionary conservation. Unfortunately, not all microRNA target sites are conserved or adhere to exact seed complementary, and relying on site accessibility does not guarantee that the interaction exists. Moreover, the study of regulatory interactions composed of the same tissue expression data for microRNAs and mRNAs is necessary to understand the specificity of regulation and function. We developed MicroTarget to predict a microRNA–gene regulatory network using heterogeneous data sources, especially gene and microRNA expression data. First, MicroTarget employs expression data to learn a candidate target set for each microRNA. Then, it uses sequence data to provide evidence of direct interactions. MicroTarget scores and ranks the predicted targets based on a set of features. The predicted targets overlap with many of the experimentally validated ones. Our results indicate that using expression data in target prediction is more accurate in terms of specificity and sensitivity. Available at: https://bioinformatics.cs.vt.edu/~htorkey/microTarget .

Independent prognostic miRNAs for bladder urothelial carcinoma

Bladder cancer is the most common malignant tumor of the urinary system, and it is also an important cause of death by cancer globally. Increasing number of studies have shown that miRNAs can be used as prognostic markers for cancers. This study made use of the data available in the Cancer Genome Atlas in order to statistically analyze reported expression levels of miRNAs in samples from bladder urothelial carcinoma patients. Clinical features from a total of 399 patients and the expression data for 1,581 kinds of miRNA were included in the study. Single factor Cox regression analysis was used to identify miRNAs related to survival times for the patients. Then, through multifactors Cox regression we sorted out the independent prognostic miRNAs for the carcinoma. According to our results, 19 miRNAs were closely related to the survival times of patients with bladder urothelial carcinoma, and 3 miRNAs including hsa-mir-518b (p=0.02), hsa-mir-192 (p=0.04) and hsa-mir-7705 (p=0.04) should be useful as independent prognostic factors in patients. In addition, the survival time of those expressing high levels of hsa-mir-7705 and hsa-mir-192 was less than the survival time of those with low expression levels. However, there were no obvious differences in the survival times between high and low expressors of hsa-mir-518b. According to our results, hsa-mir-7705, hsa-mir-192 and hsa-mir-518b can be applied as independent prognostic markers for bladder urothelial carcinoma.