CLIP-based prediction of mammalian microRNA binding sites

The miRNA-target interactions: An underestimated intricacy

MicroRNAs (miRNAs) play indispensable roles in posttranscriptional gene regulation. Their cellular regulatory impact is determined not solely by their sheer number, which likely amounts to >2000 individual miRNAs in human, than by the regulatory effectiveness of single miRNAs. Although, one begins to develop an understanding of the complex mechanisms underlying miRNA-target interactions (MTIs), the overall knowledge of MTI functionality is still rather patchy. In this critical review, we summarize key features of mammalian MTIs. We especially highlight latest insights on (i) the dynamic make-up of miRNA binding sites including non-canonical binding sites, (ii) the cooperativity between miRNA binding sites, (iii) the adaptivity of MTIs through sequence modifications, (iv) the bearing of intra-cellular miRNA localization changes and (v) the role of cell type and cell status specific miRNA interaction partners. The MTI biology is discussed against the background of state-of-the-art approaches with particular emphasis on experimental strategies for evaluating miRNA functionality.

MicroRNA-22-3p displaces critical host factors from the 5' UTR and inhibits the translation of Coxsackievirus B3 RNA

Coxsackievirus B3 (CVB3) is known to cause acute myocarditis and pancreatitis in humans. We investigated the microRNAs (miRNAs) that can potentially govern the viral life cycle by binding to the untranslated regions (UTRs) of CVB3 RNA. MicroRNA-22-3p was short-listed, as its potential binding site overlapped with the region crucial for recruiting internal ribosome entry site trans-acting factors (ITAFs) and ribosomes. We demonstrate that miR-22-3p binds CVB3 5' UTR, hinders recruitment of key ITAFs on viral mRNA, disrupts the spatial structure required for ribosome recruitment, and ultimately blocks translation. Likewise, cells lacking miR-22-3p exhibited heightened CVB3 infection compared to wild type, confirming its role in controlling infection. Interestingly, miR-22-3p level was found to be increased at 4 hours post-infection, potentially due to the accumulation of viral 2A protease in the early phase of infection. 2Apro enhances the miR-22-3p level to dislodge the ITAFs from the SD-like sequence, rendering the viral RNA accessible for binding of replication factors to switch to replication. Furthermore, one of the cellular targets of miR-22-3p, protocadherin-1 (PCDH1), was significantly downregulated during CVB3 infection. Partial silencing of PCDH1 reduced viral replication, demonstrating its proviral role. Interestingly, upon CVB3 infection in mice, miR-22-3p level was found to be downregulated only in the small intestine, the primary target organ, indicating its possible role in influencing tissue tropism. It appears miR-22-3p plays a dual role during infection by binding viral RNA to aid its life cycle as a viral strategy and by targeting a proviral protein to restrict viral replication as a host response.IMPORTANCECVB3 infection is associated with the development of end-stage heart diseases. Lack of effective anti-viral treatments and vaccines for CVB3 necessitates comprehensive understanding of the molecular players during CVB3 infection. miRNAs have emerged as promising targets for anti-viral strategies. Here, we demonstrate that miR-22-3p binds to 5' UTR and inhibits viral RNA translation at the later stage of infection to promote viral RNA replication. Conversely, as host response, it targets PCDH1, a proviral factor, to discourage viral propagation. miR-22-3p also influences CVB3 tissue tropism. Deciphering the multifaced role of miR-22-3p during CVB3 infection unravels the necessary molecular insights, which can be exploited for novel intervening strategies to curb infection and restrict viral pathogenesis.

CheRRI-Accurate classification of the biological relevance of putative RNA-RNA interaction sites

BACKGROUND

RNA-RNA interactions are key to a wide range of cellular functions. The detection of potential interactions helps to understand the underlying processes. However, potential interactions identified via in silico or experimental high-throughput methods can lack precision because of a high false-positive rate.

RESULTS

We present CheRRI, the first tool to evaluate the biological relevance of putative RNA-RNA interaction sites. CheRRI filters candidates via a machine learning-based model trained on experimental RNA-RNA interactome data. Its unique setup combines interactome data and an established thermodynamic prediction tool to integrate experimental data with state-of-the-art computational models. Applying these data to an automated machine learning approach provides the opportunity to not only filter data for potential false positives but also tailor the underlying interaction site model to specific needs.

CONCLUSIONS

CheRRI is a stand-alone postprocessing tool to filter either predicted or experimentally identified potential RNA-RNA interactions on a genomic level to enhance the quality of interaction candidates. It is easy to install (via conda, pip packages), use (via Galaxy), and integrate into existing RNA-RNA interaction pipelines.

Impact of MICA 3'UTR allelic variability on miRNA binding prediction, a bioinformatic approach

MicroRNAs (miRNAs) are small non-coding RNAs that participate as powerful genetic regulators. MiRNAs can interfere with cellular processes by interacting with a broad spectrum of target genes under physiological and pathological states, including cancer development and progression. Major histocompatibility complex major histocompatibility complex class I-related chain A (MICA) belongs to a family of proteins that bind the natural-killer group 2, member D (NKG2D) receptor on Natural Killer cells and other cytotoxic lymphocytes. MICA plays a crucial role in the host's innate immune response to several disease settings, including cancer. MICA harbors various single nucleotide polymorphisms (SNPs) located in its 3'-untranslated region (3'UTR), a characteristic that increases the complexity of MICA regulation, favoring its post-transcriptional modulation by miRNAs under physiological and pathological conditions. Here, we conducted an in-depth analysis of MICA 3'UTR sequences according to each MICA allele described to date using NCBI database. We also systematically evaluated interactions between miRNAs and their putative targets on MICA 3'UTR containing SNPs using in silico analysis. Our in silico results showed that MICA SNPs rs9266829, rs 1880, and rs9266825, located in the target sequence of miRNAs hsa-miR-106a-5p, hsa-miR-17-5p, hsa-miR-20a-5p, hsa-miR-20b-5p, hsa-miR-93, hsa-miR-1207.5p, and hsa-miR-711 could modify the binding free energy between -8.62 and -18.14 kcal/mol, which may affect the regulation of MICA expression. We believe that our results may provide a starting point for further exploration of miRNA regulatory effects depending on MICA allelic variability; they may also be a guide to conduct miRNA in silico analysis for other highly polymorphic genes.

LINC01137/miR-186-5p/WWOX: a novel axis identified from WWOX-related RNA interactome in bladder cancer

Introduction: The discovery of non-coding RNA (ncRNA) dates back to the pre-genomics era, but the progress in this field is still dynamic and leverages current post-genomics solutions. WWOX is a global gene expression modulator that is scarcely investigated for its role in regulating cancer-related ncRNAs. In bladder cancer (BLCA), the link between WWOX and ncRNA remains unexplored. The description of AP-2α and AP-2γ transcription factors, known as WWOX-interacting proteins, is more commonplace regarding ncRNA but still merits investigation. Therefore, this in vitro and in silico study aimed to construct an ncRNA-containing network with WWOX/AP-2 and to investigate the most relevant observation in the context of BLCA cell lines and patients. Methods: RT-112, HT-1376, and CAL-29 cell lines were subjected to two stable lentiviral transductions. High-throughput sequencing of cellular variants (deposited in the Gene Expression Omnibus database under the GSE193659 record) enabled the investigation of WWOX/AP-2-dependent differences using various bioinformatics tools (e.g., limma-voom, FactoMineR, multiple Support Vector Machine Recursive Feature Elimination (mSVM-RFE), miRDB, Arena-Idb, ncFANs, RNAhybrid, TargetScan, Protein Annotation Through Evolutionary Relationships (PANTHER), Gene Transcription Regulation Database (GTRD), or Evaluate Cutpoints) and repositories such as The Cancer Genome Atlas (TCGA) and Cancer Cell Line Encyclopedia. The most relevant observations from cap analysis gene expression sequencing (CAGE-seq) were confirmed using real-time PCR, whereas TCGA data were validated using the GSE31684 cohort. Results: The first stage of the whole study justified focusing solely on WWOX rather than on WWOX combined with AP-2α/γ. The most relevant observation of the developed ncRNA-containing network was LINC01137, i.e., long non-coding RNAs (lncRNAs) that unraveled the core network containing UPF1, ZC3H12A, LINC01137, WWOX, and miR-186-5p, the last three being a novel lncRNA/miRNA/mRNA axis. Patients' data confirmed the LINC01137/miR-186-5p/WWOX relationship and provided a set of dependent genes (i.e., KRT18, HES1, VCP, FTH1, IFITM3, RAB34, and CLU). Together with the core network, the gene set was subjected to survival analysis for both TCGA-BLCA and GSE31684 patients, which indicated that the increased expression of WWOX or LINC01137 is favorable, similar to their combination with each other (WWOX↑ and LINC01137↑) or with MIR186 (WWOX↑/LINC01137↑ but MIR186↓). Conclusion: WWOX is implicated in the positive feedback loop with LINC01137 that sponges WWOX-targeting miR-186-5p. This novel WWOX-containing lncRNA/miRNA/mRNA axis should be further investigated to depict its relationships in a broader context, which could contribute to BLCA research and treatment.

Competing Endogenous RNA Regulatory Networks of hsa_circ_0126672 in Pathophysiology of Coronary Heart Disease

Coronary heart disease (CHD) is a global health concern, and its molecular origin is not fully elucidated. Dysregulation of ncRNAs has been linked to many metabolic and infectious diseases. This study aimed to explore the role of circRNAs in the pathogenesis of CHD and predicted a candidate circRNA that could be targeted for therapeutic approaches to the disease. circRNAs associated with CHD were identified and CHD gene expression profiles were obtained, and analyzed with GEO2R. In addition, differentially expressed miRNA target genes (miR-DEGs) were identified and subjected to functional enrichment analysis. Networks of circRNA/miRNA/mRNA and the miRNA/affected pathways were constructed. Furthermore, a miRNA/mRNA homology study was performed. We identified that hsa_circ_0126672 was strongly associated with the CHD pathology by competing for endogenous RNA (ceRNA) mechanisms. hsa_circ_0126672 characteristically sponges miR-145-5p, miR-186-5p, miR-548c-3p, miR-7-5p, miR-495-3p, miR-203a-3p, and miR-21. Up-regulation of has_circ_0126672 affected various CHD-related cellular functions, such as atherosclerosis, JAK/STAT, and Apelin signaling pathways. Our results also revealed a perfect and stable interaction for the hybrid of miR-145-5p with NOS1 and RPS6KB1. Finally, miR-145-5p had the highest degree of interaction with the validated small molecules. Henchashsa_circ_0126672 and target miRNAs, notably miR-145-5p, could be good candidates for the diagnosis and therapeutic approaches to CHD.

miR-6087 Might Regulate Cell Cycle–Related mRNAs During Cardiomyogenesis of hESCs

MicroRNAs (miRNAs) are small noncoding RNAs that act as negative regulators of gene expression at the post-transcriptional level, promoting mRNA degradation or translation repression. Despite the well-described presence of miRNAs in various human tissues, there is still a lack of information about the relationship between miRNAs and the translation regulation in human embryonic stem cells (hESCs) during cardiomyogenesis. Here, we investigate RNA-seq data from hESCs, focusing on distinct stages of cardiomyogenesis and searching for polysome-bound miRNAs that could be involved in translational regulation. We identify miR-6087 as a differentially expressed miRNA at latest steps of cardiomyocyte differentiation. We analyzed the coexpression pattern between the differentially expressed mRNAs and miR-6087, evaluating whether they are predicted targets of the miRNA. We arranged the genes into an interaction network and identified BLM, RFC4, RFC3, and CCNA2 as key genes of the network. A post hoc analysis of the key genes suggests that miR-6087 could act as a regulator of the cell cycle in hESC during cardiomyogenesis.

Characteristics of piRNAs and their comparative profiling in testes of sheep with different fertility

As a novel class of small RNAs, piRNAs are highly expressed in the animal gonads and their main known role is to inhibit transposon activity for ensuring the correctness and integrity of genome. In order to explore the characteristics of piRNAs in sheep testis and their possible regulatory roles on male reproduction, deep sequencing technology was used to sequence small RNAs and identify piRNAs in testes of sheep. The length of piRNAs in sheep testes showed a unimodal distribution between 26 and 31 nt, with a peak at 29 nt. These piRNAs exhibited obvious ping-pong signature and strand specificity. In the genome, they were mainly aligned to CDS, intron, repetitive sequence regions and unannotated regions. Furthermore, in transposon analysis, piRNAs were aligned predominantly to LINE, SINE, and LTR types of retrotransposon in sheep testes, and the piRNAs derived from each type showed obvious ping-pong signature. The piRNA clusters identified in sheep testes were mainly distributed on chromosomes 3, 7, 15, 17, 18 and 20. The results combining semen determination with pathway enrichment analysis implied that differentially expressed piRNAs between the testes of rams with different fertility might participate in spermatogenesis by regulating multiple pathways closely related to stabilization of blood-testis barrier and renewal and differentiation of spermatogonial stem cell. Taken together, the study provided new insights into the characteristics, origin and expression patterns of piRNAs in sheep testes tissue, which would help us better understand the role of piRNAs in sheep reproduction.

Regulation of human UDP-glycosyltransferase (UGT) genes by miRNAs

The human UGT gene superfamily is divided into four subfamilies (UGT1, UGT2, UGT3 and UGT8) that encodes 22 functional enzymes. UGTs are critical for the metabolism and clearance of numerous endogenous and exogenous compounds, including steroid hormones, bile acids, bilirubin, fatty acids, carcinogens, and therapeutic drugs. Therefore, the expression and activities of UGTs are tightly regulated by multiple processes at the transcriptional, post-transcriptional and post-translational levels. During recent years, nearly twenty studies have investigated the post-transcriptional regulation of UGT genes by miRNAs using human cancer cell lines (predominantly liver cancer). Overall, 14 of the 22 UGT mRNAs (1A1, 1A3, 1A4, 1A6, 1A8, 1A9, 1A10, 2A1, 2B4, 2B7, 2B10, 2B15, 2B17, UGT8) have been shown to be regulated by various miRNAs through binding to their respective 3' untranslated regions (3'UTRs). Three 3'UTRs (UGT1A, UGT2B7 and UGT2B15) contain the largest number of functional miRNA target sites; in particular, the UGT1A 3'UTR contains binding sites for 12 miRNAs (548d-5p, 183-5p, 214-5p, 486-3p, 200a-3p, 491-3p, 141-3p, 298, 103b, 376b-3p, 21-3p, 1286). Although all nine UGT1A family members have the same 3'UTR, these miRNA target sites appear to be functional in an isoform-specific and cellular context-dependent manner. Collectively, these observations demonstrate that miRNAs represent important post-transcriptional regulators of the UGT gene superfamily. In this article, we present a comprehensive review of reported UGT/miRNA regulation studies, describe polymorphisms within functional miRNA target sites that may affect their functionalities, and discuss potential cooperative and competitive regulation of UGT mRNAs by miRNAs through adjacently located miRNA target sites.

N 6-methyladenosine enhances post-transcriptional gene regulation by microRNAs

MOTIVATION

N ⁶-methyladenosine (m⁶A) is the most prevalent modification in eukaryotic messenger RNAs. MicroRNAs (miRNAs) are abundant post-transcriptional regulators of gene expression. Correlation between m⁶A and miRNA-targeting sites has been reported to suggest possible involvement of m⁶A in miRNA-mediated gene regulation. However, it is unknown what the regulatory effects might be. In this study, we performed comprehensive analyses of high-throughput data on m⁶A and miRNA target binding and regulation.

RESULTS

We found that the level of miRNA-mediated target suppression is significantly enhanced when m⁶A is present on target mRNAs. The evolutionary conservation for miRNA-binding sites with m⁶A modification is significantly higher than that for miRNA-binding sites without modification. These findings suggest functional significance of m⁶A modification in post-transcriptional gene regulation by miRNAs. We also found that methylated targets have more stable structure than non-methylated targets, as indicated by significantly higher GC content. Furthermore, miRNA-binding sites that can be potentially methylated are significantly less accessible without methylation than those that do not possess potential methylation sites. Since either RNA-binding proteins or m⁶A modification by itself can destabilize RNA structure, we propose a model in which m⁶A alters local target secondary structure to increase accessibility for efficient binding by Argonaute proteins, leading to enhanced miRNA-mediated regulation.

AVAILABILITY AND IMPLEMENTATION

N/A.

Predicting sequence and structural features of effective piRNA target binding sites

Piwi-interacting RNA (piRNA) targets are usually identified through base pairing between the piRNA seed region and complementary bases on the target mRNAs, which often results in false predictions. Crosslinking immunoprecipitation (CLIP) study emerges as a promising method that enables accurate identification of PIWI-clade-based targets containing RNA-binding sites. In the present study, we have analyzed the piRNA-target CLIP-seq datasets to uncover the additional characteristic features of piRNA targets. We studied important sequence and structural features using IP⁺ and IP^- set targets that might enhance the accuracy of target site predictions. Analysis has revealed substantial enrichment of AU in target sites as well as in and around the 30 nts upstream and downstream of target sites in IP⁺ set relative to IP^- set that might be contributing to lowering the minimal folding energy of target sites of IP⁺ + set that might be easing the base pairing between piRNA and their targets. We have also found a lower MFE threshold (en) and higher miRanda score for piRNA targets. Interestingly, we have found that majority of the target sites are residing within 3'UTR, suggesting 3'UTR as a preferential target site like that of miRNA targets. Thus, we hypothesize that our findings on additional key features of piRNA target sites might be valuable in identifying the potential targets of piRNA accurately, which will aid in decrypting their functional importance. This article is protected by copyright. All rights reserved.

Identifying a novel biological mechanism for alcohol addiction associated with circRNA networks acting as potential miRNA sponges

Our lab and others have shown that chronic alcohol use leads to gene and miRNA expression changes across the mesocorticolimbic (MCL) system. Circular RNAs (circRNAs) are noncoding RNAs that form closed-loop structures and are reported to alter gene expression through miRNA sequestration, thus providing a potentially novel neurobiological mechanism for the development of alcohol dependence (AD). Genome-wide expression of circRNA was assessed in the nucleus accumbens (NAc) from 32 AD-matched cases/controls. Significant circRNAs (unadj. p ≤ 0.05) were identified via regression and clustered in circRNA networks via weighted gene co-expression network analysis (WGCNA). CircRNA interactions with previously generated mRNA and miRNA were detected via correlation and bioinformatic analyses. Significant circRNAs (N = 542) clustered in nine significant AD modules (FWER p ≤ 0.05), within which we identified 137 circRNA hubs. We detected 23 significant circRNA-miRNA-mRNA interactions (FDR ≤ 0.10). Among these, circRNA-406742 and miR-1200 significantly interact with the highest number of mRNA, including genes associated with neuronal functioning and alcohol addiction (HRAS, PRKCB, HOMER1, and PCLO). Finally, we integrate genotypic information that revealed 96 significant circRNA expression quantitative trait loci (eQTLs) (unadj. p ≤ 0.002) that showed significant enrichment within recent alcohol use disorder (AUD) and smoking genome-wide association study (GWAS). To our knowledge, this is the first study to examine the role of circRNA in the neuropathology of AD. We show that circRNAs impact mRNA expression by interacting with miRNA in the NAc of AD subjects. More importantly, we provide indirect evidence for the clinical importance of circRNA in the development of AUD by detecting a significant enrichment of our circRNA eQTLs among GWAS of substance abuse.

In Silico and In Vitro Analyses Validate Human MicroRNAs Targeting the SARS-CoV-2 3'-UTR

COVID-19 pandemic is caused by betacoronavirus SARS-CoV-2. The genome of this virus is composed of a single strand of RNA with 5' and 3'-UTR flanking a region of protein-coding ORFs closely resembling cells' mRNAs. MicroRNAs are endogenous post-transcriptional regulators that target mRNA to modulate protein expression and mediate cellular functions, including antiviral defense. In the present study, we carried out a bioinformatics screening to search for endogenous human microRNAs targeting the 3'-UTR of SARS-CoV-2. Results from the computational techniques allowed us to identify 10 potential candidates. The capacity of 3 of them, together with hsa-miR-138-5p, to target the SARS-CoV-2 3'-UTR was validated in vitro by gene reporter assays. Available information indicates that two of these microRNAs, namely, hsa-miR-3941 and hsa-miR-138-5p, combine effective targeting of SARS-CoV-2 genome with complementary antiviral or protective effects in the host cells that make them potential candidates for therapeutic treatment of most, if not all, COVID-19 variants known to date. All information obtained while conducting the present analysis is available at Open Science Framework repository.

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 .

An intelligent Bayesian hybrid approach to help autism diagnosis

This paper proposes a Bayesian hybrid approach based on neural networks and fuzzy systems to construct fuzzy rules to assist experts in detecting features and relations regarding the presence of autism in human beings. The model proposed in this paper works with a database generated through mobile devices that deals with diagnoses of autistic characteristics in human beings who answer a series of questions in a mobile application. The Bayesian model works with the construction of Gaussian fuzzy neurons in the first and logical neurons in the second layer of the model to form a fuzzy inference system connected to an artificial neural network that activates a robust output neuron. The new fuzzy neural network model was compared with traditional state-of-the-art machine learning models based on high-dimensional based on real-world data sets comprising the autism occurrence in children, adults, and adolescents. The results (97.73- Children/94.32-Adolescent/97.28-Adult) demonstrate the efficiency of our new method in determining children, adolescents, and adults with autistic traits (being among the top performers among all ML models tested), can generate knowledge about the dataset through fuzzy rules.

MicroRNAs Instruct and Maintain Cell Type Diversity in the Nervous System

Characterizing the diverse cell types that make up the nervous system is essential for understanding how the nervous system is structured and ultimately how it functions. The astonishing range of cellular diversity found in the nervous system emerges from a small pool of neural progenitor cells. These progenitors and their neuronal progeny proceed through sequential gene expression programs to produce different cell lineages and acquire distinct cell fates. These gene expression programs must be tightly regulated in order for the cells to achieve and maintain the proper differentiated state, remain functional throughout life, and avoid cell death. Disruption of developmental programs is associated with a wide range of abnormalities in brain structure and function, further indicating that elucidating their contribution to cellular diversity will be key to understanding brain health. A growing body of evidence suggests that tight regulation of developmental genes requires post-transcriptional regulation of the transcriptome by microRNAs (miRNAs). miRNAs are small non-coding RNAs that function by binding to mRNA targets containing complementary sequences and repressing their translation into protein, thereby providing a layer of precise spatial and temporal control over gene expression. Moreover, the expression profiles and targets of miRNAs show great specificity for distinct cell types, brain regions and developmental stages, suggesting that they are an important parameter of cell type identity. Here, we provide an overview of miRNAs that are critically involved in establishing neural cell identities, focusing on how miRNA-mediated regulation of gene expression modulates neural progenitor expansion, cell fate determination, cell migration, neuronal and glial subtype specification, and finally cell maintenance and survival.

In silico analysis of non-coding RNAs and putative target genes implicated in metabolic syndrome

Regulation of gene expression is vital to maintain normal cellular functions and its dysregulation leads to molecular pathogenesis of many diseases and disorders. Non-coding RNAs regulate the expression of approximately 60% of protein-coding genes and their malfunction contribute to the development of numerous diseases. The involvement of variant forms of circulating non-coding RNAs in diseases has been established. However, their function as biomarkers or therapeutic targets in metabolic disorders are underexploited. The aim of this study was to predict therapeutic targets and construction of biomarker panel for early detection of metabolic syndrome (MS). Non-coding RNAs including circular RNAs (circRNAs), long chain non-coding RNAs (lncRNA) and micro RNAs (miRNAs) were extracted from intensive literature search and experimentally supported databases. Raw data of gene expression profiles of MS were obtained from the GEO dataset and analyzed to get differentially expressed genes (DEGs). Functional enrichment analysis, network illustration of non-coding RNAs and predicted target DEGs were performed. Furthermore, a few numbers of miRNAs targeted DEGs were subjected to homology study. The strong association of hsa-miR-548c-3p, hsa-miR-579-3p, hsa-miR-17-5p and hsa-miR-320a was observed with the pathogenesis of MS. It includes the regulation of genes in glucose and lipid homeostasis, MAPKK activity, regulation of inflammatory responses and many signaling pathways such as insulin resistance, JAK/STAT and mTOR. Finally, interactions of hsa-miR-17-5p:STAT3, hsa-miR-320:JAK2, hsa-miR-320:S6K and hsa-let-7:DVL hybrids were predicted. Results of this study suggest the designing of a biomarker panel to detect early onset and molecular approach for the management of MS.

An improvement of ComiR algorithm for microRNA target prediction by exploiting coding region sequences of mRNAs

MicroRNA are small non-coding RNAs that post-transcriptionally regulate the expression levels of messenger RNAs. MicroRNA regulation activity depends on the recognition of binding sites located on mRNA molecules. ComiR is a web tool realized to predict the targets of a set of microRNAs, starting from their expression profile. ComiR was trained with the information regarding binding sites in the 3’utr region, by using a reliable dataset containing the targets of endogenously expressed microRNA in D. melanogaster S2 cells. This dataset was obtained by comparing the results from two different experimental approaches, i.e., inhibition, and immunoprecipitation of the AGO1 protein--a component of the microRNA induced silencing complex.

In this work, we tested whether including coding region binding sites in ComiR algorithm improves the performance of the tool in predicting microRNA targets. We focused the analysis on the D. melanogaster species and updated the ComiR underlying database with the currently available releases of mRNA and microRNA sequences. As a result, we find that ComiR algorithm trained with the information related to the coding regions is more efficient in predicting the microRNA targets, with respect to the algorithm trained with 3’utr information. On the other hand, we show that 3’utr based predictions can be seen as complementary to the coding region based predictions, which suggests that both predictions, from 3’utr and coding regions, should be considered in comprehensive analysis.

Furthermore, we observed that the lists of targets obtained by analyzing data from one experimental approach only, that is, inhibition or immunoprecipitation of AGO1, are not reliable enough to test the performance of our microRNA target prediction algorithm. Further analysis will be conducted to investigate the effectiveness of the tool with data from other species, provided that validated datasets, as obtained from the comparison of RISC proteins inhibition and immunoprecipitation experiments, will be available for the same samples. Finally, we propose to upgrade the existing ComiR web-tool by including the coding region based trained model, available together with the 3’utr based one.

The Complex Landscape of PTEN mRNA Regulation

Phosphatase and tensin homolog deleted on chromosome 10 (PTEN) is a key tumor suppressor in the development and progression of different tumor types. Emerging data indicate that small reductions in PTEN protein levels can promote cancer. PTEN protein levels are tightly controlled by a plethora of mechanisms beginning with epigenetic and transcriptional regulation and ending with control of protein synthesis and stability. PTEN messenger RNA (mRNA) is also subject to exquisite regulation by microRNAs, coding and long noncoding RNAs, and RNA-binding proteins. Additionally, PTEN mRNA is markedly influenced by alternative splicing and variable polyadenylation. Herein we provide a synoptic description of the current understanding of the complex regulatory landscape of PTEN mRNA regulation including several specific processes that modulate its stability and expression, in the context of PTEN loss-associated cancers.

Maternal blood EBF1-based microRNA transcripts as biomarkers for detecting risk of spontaneous preterm birth: a nested case-control study

Objective: Both genetic variants and maternal blood mRNA levels of EBF1 gene have been linked to sPTB. Animal and human studies suggest that specific EBF1-based miRNAs are involved in various physiological and pathophysiological processes. However, to date, we did not find any reports of EBF1-based miRNAs or miRNA transcripts in relation to sPTB. We therefore aimed to examine whether maternal blood early B cell factor 1 (EBF1) gene-based microRNA (miRNA) transcripts can be used for detecting risk of spontaneous preterm birth (sPTB).Methods: We conducted a nested case-control study within a Canadian cohort consisting of 1878 singleton pregnancies enrolled from May 2008 to December 2010 in Calgary, Alberta, Canada. We used a public gene expression dataset (GSE59491) derived from maternal blood in trimesters 2-3 that included women with sPTB (n = 51) and term births (n = 106) matched for maternal age, race/ethnicity, pre-pregnancy body mass index, smoking during pregnancy, and parity within the Canadian cohort. Two bioinformatics tools, miRWalk and STarMirDB, with different algorithms were applied to retrieve miRNA transcripts that putatively target the EBF1 gene (i.e. EBF1-based). Limma moderated t-tests were used to examine differentially expressed (DE) miRNA transcripts (sPTB vs term) within trimesters. Logistic regression models with miRNA transcript tertiles were applied to assess threshold associations between candidate miRNA transcripts' levels and sPTB. Receiver operating characteristic (ROC) analyses were used to identify the maximum Youden Index and its corresponding optimal sensitivity/specificity cut-point of EBF1-based miRNA transcripts for classifying sPTB, and to compare the classification performance of a linear combination (score) of miRNA transcripts with that of individual miRNA transcripts. A five-fold cross-validation was applied to examine the possible overfitting problem of the final ROC model.Results: Four maternal blood EBF1-based miRNA transcripts (MIR4266, MIR1251, MIR601, MIR3612) in the 3rd trimester were significantly associated with sPTB. The odds ratios (95%CIs) for highest versus lowest tertile of the four miRNA transcripts were 3.01-5.25(1.21-13.14, p ≤ .018). The combined 4-miRNA transcripts' score significantly improved the classification of sPTB compared to individual miRNA transcripts (AUC increased from 0.65-0.69 to 0.82, p ≤ .0034) and showed a sensitivity for sPTB of 0.81 and a specificity of 0.72. The final ROC model of the EBF1-based 4 miRNA transcripts' score in cases and controls had no significant overfitting issue.Conclusions: Maternal blood EBF1-based miRNA transcripts may, along with other biomarkers, be useful in screening for sPTB risk in 3rd trimester. Our results also provide clues for further study of potential molecular mechanisms underlying the relationship between EBF1 gene and sPTB, e.g. connecting genetic variants, mRNA expression, and miRNA regulation.

A Supervised Ensemble Approach for Sensitive microRNA Target Prediction

MicroRNAs, a class of small non-coding RNAs, regulate important biological functions via post-transcriptional regulation of messenger RNAs (mRNAs). Despite rapid development in miRNA research, precise experimental methods to determine miRNA target interactions are still lacking. This motivated us to explore the in silico target interaction features and incorporate them in predictive modeling. We propose a systematic approach towards developing a sensitive miRNA target prediction model to explore the interplay of target recognition features. In the first step, we have employed a supervised ensemble under-sampling approach to address the problem of imbalance in the training dataset due to a larger number of negative instances. Various feature selection techniques were evaluated to obtain the optimal feature subset that best recognizes the true miRNA-mRNA targets. In the second step, we have built our optimal model, miRTPred, a novel blending ensemble-based approach that combines the predictions of the best performing traditional and classical ensemble models, through a weighted voting classifier, achieving a sensitivity of 87 percent and F1-score of 0.88 for 3'UTR region of the mRNA transcript. miRTPred outperforms popular machine learning (ML) and non-ML approaches to target prediction algorithms. miRTPred is freely available at http://bicresources.jcbose.ac.in/zhumur/mirtpred.

miR-140-3p exhibits repressive functions on preosteoblast viability and differentiation by downregulating MCF2L in osteoporosis

Previous research manifested that miR-140-3p was a latent biomarker for osteoporosis. Nevertheless, the mechanism of miR-140-3p in osteoporosis is still not clear and needs ulteriorly studying. The purpose of our paper was to ulteriorly probe the underlying mechanism of miR-140-3p on osteoporosis. Firstly, based on the data acquired from GEO database, we found that miR-140-3p was highly expressed; meanwhile, MCF2L was lowly expressed in osteoporosis patients. Upregulation/downregulation of miR-140-3p by miR-140-3p mimic/inhibitor restrained/promoted MC3T3-E1 cell viability and differentiation. However, miR-140-3p over-expression/downregulation accelerated/repressed MC3T3-E1 cell apoptosis. MCF2L was forecasted as a target of miR-140-3p by miRanda, miRWalk, and TargetScan miRNA target gene prediction software. Luciferase reporter assay confirmed that MCF2L could be directly targeted by miR-140-3p. Moreover, we identified that the expression of MCF2L was negatively regulated by miR-140-3p. From rescue assays, we discovered that knockdown of MCF2L weakened the promoting influence of miR-140-3p ablation on MC3T3-E1 cell viability and differentiation, and receded the suppressing impact of miR-140-3p reduction on MC3T3-E1 cell apoptosis. Above all, this research disclosed that miR-140-3p repressed preosteoblast viability and differentiation while promoted preosteoblast apoptosis via targeting MCF2L. Our discoveries might afford a theoretical basis of developing a latent novel target for osteoporosis therapy.

Systematic evaluation of the microRNAome through miR-CATCHv2.0 identifies positive and negative regulators of BRAF-X1 mRNA

Here we present miR-CATCHv2.0, an implemented experimental method that allows the identification of the microRNA species directly bound to an RNA of interest. After cross-linking of microRNA::RNA::Ago2 complexes using formaldehyde, the RNA is fragmented using sonication and then subjected to affinity purification using two sets of biotinylated tiling probes (ODD and EVEN). Finally, enriched microRNA species are retrieved by means of small RNA sequencing coupled with an ad hoc analytical workflow. In BRAFV600E mutant A375 melanoma cells, miR-CATCHv2.0 allowed us to identify 20 microRNAs that target X1, the most abundant isoform of BRAF mRNA. These microRNAs fall into different functional classes, according to the effect that they exert (decrease/increase in BRAFV600E mRNA and protein levels) and to the mechanism they use to achieve it (destabilization/stabilization of X1 mRNA or decrease/increase in its translation). microRNA-induced variations in BRAFV600E protein levels are most of the times coupled to consistent variations in pMEK levels, in melanoma cell proliferation in vitro and in sensitivity to the BRAF inhibitor vemurafenib in a xenograft model in zebrafish. However, microRNAs exist that uncouple the degree of activation of the ERK pathway from the levels of BRAFV600E protein. Our study proposes miR-CATCHv2.0 as an effective tool for the identification of direct microRNA-target interactions and, by using such a tool, unveils the complexity of the post-transcriptional regulation to which BRAFV600E and the ERK pathway are subjected in melanoma cells.

miR-197-5p inhibits sarcomagenesis and induces cellular senescence via repression of KIAA0101

The abnormal expressions of microRNAs (miRNAs) are known to be associated with various pathophysiological processes that lead to the development of a plethora of diseases including cancer. Among several miRNAs studied so far, miR-197 has been reported to play a vital role either as an oncogene or tumor suppressor in different cancers. However, its role in carcinogenesis of fibrosarcoma has not yet been elucidated. Therefore, the current study investigated the role of miR-197-5p, which is significantly downregulated in HT1080 fibrosarcoma cells compared to IMR90-tert fibroblast cells. The transient overexpression of miR-197-5p causes a significant decrease in viability and proliferation of fibrosarcoma cells in both concentration- and time-dependent manners. Interestingly, we did not observe any significant changes in cell cycle pattern or apoptotic cell populations, but rather noticed cellular senescence of fibrosarcoma cells upon overexpression of miR-197-5p. Further, this miRNA suppresses the metastatic properties, such as migration, invasion, and anchorage-independent growth of fibrosarcoma possibly through targeting KIAA0101, which is a proliferating cell nuclear antigen-associated factor and overexpressed in the malignancy. In nutshell, our result revealed that miR-197-5p acts as an oncosuppressor miRNA in fibrosarcoma through target regulation of KIAA0101, which can be exploited for developing RNA-based therapeutic strategies for the cure of this malignancy.

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 ).

A Practical Guide to miRNA Target Prediction

MicroRNAs (miRNAs) are small endogenous noncoding RNA molecules that posttranscriptionally regulate gene expression. Since their discovery, a huge number of miRNAs have been identified in a wide range of species. Through binding to the 3' UTR of mRNA, miRNA can block translation or stimulate degradation of the targeted mRNA, thus affecting nearly all biological processes. Prediction and identification of miRNA target genes is crucial toward understanding the biology of miRNAs. Currently, a number of sophisticated bioinformatics approaches are available to perform effective prediction of miRNA target sites. In this chapter, we present the major features that most algorithms take into account to efficiently predict miRNA target: seed match, free energy, conservation, target site accessibility, and contribution of multiple binding sites. We also give an overview of the frequently used bioinformatics tools for miRNA target prediction. Understanding the basis of these prediction methodologies may help users to better select the appropriate tools and analyze their output.

Sfold Tools for MicroRNA Target Prediction

Computational prediction of miRNA binding sites on target mRNAs facilitates experimental investigation of miRNA functions. In this chapter, we describe STarMir and STarMirDB, two application modules of the Sfold RNA package. STarMir is a Web server for performing miRNA binding site predictions for mRNA and target sequences submitted by users. STarMirDB is a database of precomputed transcriptome-scale predictions. Both STarMir and STarMirDB provide comprehensive sequence, thermodynamic, and target structure features, a logistic probability as a measure of confidence for each predicted site, and a publication-quality diagram of the predicted miRNA-target hybrid. In addition, STarMir now offers a new quantitative score to address combined regulatory effects of multiple seed and seedless sites. This score provides a quantitative measure of the overall regulatory effects of both seed and seedless sites on the target. STarMir and STarMirDB are freely available to all through the Sfold Web application server at http://sfold.wadsworth.org .

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.

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.

miRNA-mediated 'tug-of-war' model reveals ceRNA propensity of genes in cancers

Competing endogenous RNA (ceRNA) are transcripts that cross‐regulate each other at the post‐transcriptional level by competing for shared microRNA response elements (MREs). These have been implicated in various biological processes impacting cell‐fate decisions and diseases including cancer. There are several studies that predict possible ceRNA pairs by adopting various machine‐learning and mathematical approaches; however, there is no method that enables us to gauge as well as compare the propensity of the ceRNA of a gene and precisely envisages which among a pair exerts a stronger pull on the shared miRNA pool. In this study, we developed a method that uses the ‘tug of war of genes’ concept to predict and quantify ceRNA potential of a gene for the shared miRNA pool in cancers based on a score represented by SoCeR (score of competing endogenous RNA). The method was executed on the RNA‐Seq transcriptional profiles of genes and miRNA available at TCGA along with CLIP‐supported miRNA‐target sites to predict ceRNA in 32 cancer types which were validated with already reported cases. The proposed method can be used to determine the sequestering capability of the gene of interest as well as in ranking the probable ceRNA candidates of a gene. Finally, we developed standalone applications (SoCeR tool) to aid researchers in easier implementation of the method in analysing different data sets or diseases.

The deregulated microRNAome contributes to the cellular response to aneuploidy

BACKGROUND

Aneuploidy, or abnormal chromosome numbers, severely alters cell physiology and is widespread in cancers and other pathologies. Using model cell lines engineered to carry one or more extra chromosomes, it has been demonstrated that aneuploidy per se impairs proliferation, leads to proteotoxic as well as replication stress and triggers conserved transcriptome and proteome changes.

RESULTS

In this study, we analysed for the first time miRNAs and demonstrate that their expression is altered in response to chromosome gain. The miRNA deregulation is independent of the identity of the extra chromosome and specific to individual cell lines. By cross-omics analysis we demonstrate that although the deregulated miRNAs differ among individual aneuploid cell lines, their known targets are predominantly associated with cell development, growth and proliferation, pathways known to be inhibited in response to chromosome gain. Indeed, we show that up to 72% of these targets are downregulated and the associated miRNAs are overexpressed in aneuploid cells, suggesting that the miRNA changes contribute to the global transcription changes triggered by aneuploidy. We identified hsa-miR-10a-5p to be overexpressed in majority of aneuploid cells. Hsa-miR-10a-5p enhances translation of a subset of mRNAs that contain so called 5'TOP motif and we show that its upregulation in aneuploids provides resistance to starvation-induced shut down of ribosomal protein translation.

CONCLUSIONS

Our work suggests that the changes of the microRNAome contribute on one hand to the adverse effects of aneuploidy on cell physiology, and on the other hand to the adaptation to aneuploidy by supporting translation under adverse conditions.

microRNA-558 facilitates the expression of hypoxia-inducible factor 2 alpha through binding to 5'-untranslated region in neuroblastoma

Neuroblastoma (NB) is the most common extracranial solid tumor in childhood. Our previous studies have shown that hypoxia-inducible factor 2 alpha (HIF-2α), one member of the bHLH-PAS transcription factor family, facilitates the progression of NB under non-hypoxic conditions. However, the mechanisms underlying HIF-2α expression in NB still remain largely unknown. Herein, through analyzing the computational algorithm programs, we identified microRNA-558 (miR-558) as a crucial regulator of HIF-2α expression in NB. We demonstrated that miR-558 promoted the expression of HIF-2α at translational levels in NB cells through recruiting Argonaute 2 (AGO2). Mechanistically, miR-558 directly bound with its complementary site within 5′-untranslated region (5′-UTR) to facilitate the binding of AGO2 to eukaryotic translation initiation factor 4E (eIF4E) binding protein 1, resulting in increased eIF4E enrichment and HIF-2α translation. In addition, miR-558 promoted the growth, invasion, metastasis, and angiogenesis of NB cells in vitro and in vivo, and these biological features were rescued by knockdown of AGO2, eIF4E, or HIF-2α. In clinical NB specimens, miR-558, AGO2, and eIF4E were highly expressed and positively correlated with HIF-2α expression. Patients with high miR-558, HIF-2α, AGO2, or eIF4E levels had lower survival probability. Taken together, these results demonstrate that miR-558 facilitates the expression of HIF-2α through bindingto its 5′-UTR, thus promoting the tumorigenesis and aggressiveness of NB.

Probing instructions for expression regulation in gene nucleotide compositions

Gene expression is orchestrated by distinct regulatory regions to ensure a wide variety of cell types and functions. A challenge is to identify which regulatory regions are active, what are their associated features and how they work together in each cell type. Several approaches have tackled this problem by modeling gene expression based on epigenetic marks, with the ultimate goal of identifying driving regions and associated genomic variations that are clinically relevant in particular in precision medicine. However, these models rely on experimental data, which are limited to specific samples (even often to cell lines) and cannot be generated for all regulators and all patients. In addition, we show here that, although these approaches are accurate in predicting gene expression, inference of TF combinations from this type of models is not straightforward. Furthermore these methods are not designed to capture regulation instructions present at the sequence level, before the binding of regulators or the opening of the chromatin. Here, we probe sequence-level instructions for gene expression and develop a method to explain mRNA levels based solely on nucleotide features. Our method positions nucleotide composition as a critical component of gene expression. Moreover, our approach, able to rank regulatory regions according to their contribution, unveils a strong influence of the gene body sequence, in particular introns. We further provide evidence that the contribution of nucleotide content can be linked to co-regulations associated with genome 3D architecture and to associations of genes within topologically associated domains.

A genetic variation in microRNA target site of ETS2 is associated with clinical outcomes of paclitaxel-cisplatin chemotherapy in non-small cell lung cancer

The present study was performed to investigate the association of single nucleotide polymorphisms (SNPs) located in the miRNA target sites with the clinical outcomes of first line paclitaxel-cisplatin chemotherapy in advanced NSCLC. Eighty SNPs in miRNA binding sites of cancer related genes selected from 18,500 miRNA:target bindings in crosslinking, ligation, and sequencing of hybrids (CLASH) data were investigated in 379 advanced NSCLC patients using a sequenom mass spectrometry-based genotype assay. qRT-PCR and luciferase assay were conducted to examine functional relevance of potentially functional SNPs in miRNA binding sites. Of the 80 SNPs analyzed, 16 SNPs were significantly associated with the clinical outcomes after chemotherapy. Among these, ANAPC1 rs3814026C>T, ETS2 rs461155A>G, SORBS1 rs7081076C>A and POLR2A rs2071504C>T could predict both chemotherapy response and survival. Notably, ETS2 rs461155A>G was significantly associated with decreased ETS2 mRNA expression in both tumor and paired normal lung tissues (Ptrend = 4 × 10⁻⁷, and 3 × 10⁻⁴, respectively). Consistently, a decreased expression of the reporter gene for the G allele of rs461155 compared with the A allele was observed by luciferase assay. These findings suggest that the four SNPs, especially ETS2 rs461155A>G, could be used as biomarkers predicting the clinical outcomes of NSCLC patients treated with first-line paclitaxel-cisplatin chemotherapy.

Post-transcriptional gene silencing mediated by microRNAs is controlled by nucleoplasmic Sfpq

There is a growing body of evidence about the presence and the activity of the miRISC in the nucleus of mammalian cells. Here, we show by quantitative proteomic analysis that Ago2 interacts with the nucleoplasmic protein Sfpq in an RNA-dependent fashion. By a combination of HITS-CLIP and transcriptomic analyses, we demonstrate that Sfpq directly controls the miRNA targeting of a subset of binding sites by local binding. Sfpq modulates miRNA targeting in both nucleoplasm and cytoplasm, indicating a nucleoplasmic commitment of Sfpq-target mRNAs that globally influences miRNA modes of action. Mechanistically, Sfpq binds to a sizeable set of long 3′UTRs forming aggregates to optimize miRNA positioning/recruitment at selected binding sites, including let-7a binding to Lin28A 3′UTR. Our results extend the miRNA-mediated post-transcriptional gene silencing into the nucleoplasm and indicate that an Sfpq-dependent strategy for controlling miRNA activity takes place in cells, contributing to the complexity of miRNA-dependent gene expression control.

MicroRNAs have been best characterized for their functions in the cytoplasm; however, there is growing evidence of a nuclear localized role. Here, the authors identify Sfpq as an Ago2-interacting protein that modulates miRNA activity in both the nucleus and cytoplasm.

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.

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.

miR-429 inhibits metastasis by targeting KIAA0101 in Soft Tissue Sarcoma

Soft tissue sarcomas (STS) are a heterogeneous group of rare tumors with high metastatic potential. There being only a handful of publication on metastasis of STS, we investigated the miRNA mediated target gene regulations in modulating the metastatic processes in this cancer. In this study, we amalgamated gene and miRNA expression profiles of high-grade STS samples with miRNA target predictions and identified miR-429 targeting KIAA0101 as a novel pair, which remain unexplored in STS metastasis. We validated their expression in metastatic fibrosarcoma cell line, HT1080 and performed several functional assays using miRNA mimics and KIAA0101 over-expression vector to confirm their role in metastasis. We observed miR-429 is downregulated in HT1080 cells and acting as an anti-metastatic miRNA that inhibited proliferation, migration, anchorage independent growth and invasion by de-repressing KIAA0101. Moreover, the renilla luciferase reporter assay confirmed that miR-429 targets KIAA0101 by binding to its 3^/UTR and influence its expression. Taken together, our work demonstrated miR-429 mediates deregulation of KIAA0101 by acting as an anti-metastatic miRNA that targets KIAA0101 pro-metastatic gene during metastasis of STS.

Altered gene expression in late-onset Alzheimer's disease due to SNPs within 3'UTR microRNA response elements

Late-onset Alzheimer's disease (LOAD) is a progressive and fatal neurodegenerative disease found in people older than 65years of age. Disease etiology is complex, as susceptibility has been linked to multiple gene variants conferred by single nucleotide polymorphisms (SNPs). However, the molecular mechanisms by which SNPs contribute to LOAD pathogenesis have not been extensively studied, particularly for SNPs within the 3' untranslated regions (3'UTRs), the hubs for microRNA binding. Therefore, we screened for SNPs within the 3'UTRs of LOAD-associated genes that may create or destroy microRNA response elements (MREs) and thus alter gene expression. This investigation adopted an in-silico approach that integrated structural and thermodynamic features of miRNA target binding with screening using CLIP-seq data, followed by network analysis. This strategy identified three 3'UTR SNPs, rs10876135, rs5848, and rs5786996 that may alter the respective binding sites for the miRNAs hsa-miR-197-5p, hsa-miR-185-5p, and hsa-miR-34a-5p, all of which are upregulated in LOAD. The functional significance of these MRE-SNPs was assessed by potential regulation of biological networks known to be associated with LOAD. This is the first study to demonstrate a possible role for above 3'UTR MRE-SNPs in aberrant expression of target genes with functional consequences for LOAD.

microRNA-122 target sites in the hepatitis C virus RNA NS5B coding region and 3' untranslated region: function in replication and influence of RNA secondary structure

We have analyzed the binding of the liver-specific microRNA-122 (miR-122) to three conserved target sites of hepatitis C virus (HCV) RNA, two in the non-structural protein 5B (NS5B) coding region and one in the 3' untranslated region (3'UTR). miR-122 binding efficiency strongly depends on target site accessibility under conditions when the range of flanking sequences available for the formation of local RNA secondary structures changes. Our results indicate that the particular sequence feature that contributes most to the correlation between target site accessibility and binding strength varies between different target sites. This suggests that the dynamics of miRNA/Ago2 binding not only depends on the target site itself but also on flanking sequence context to a considerable extent, in particular in a small viral genome in which strong selection constraints act on coding sequence and overlapping cis-signals and model the accessibility of cis-signals. In full-length genomes, single and combination mutations in the miR-122 target sites reveal that site 5B.2 is positively involved in regulating overall genome replication efficiency, whereas mutation of site 5B.3 showed a weaker effect. Mutation of the 3'UTR site and double or triple mutants showed no significant overall effect on genome replication, whereas in a translation reporter RNA, the 3'UTR target site inhibits translation directed by the HCV 5'UTR. Thus, the miR-122 target sites in the 3'-region of the HCV genome are involved in a complex interplay in regulating different steps of the HCV replication cycle.

miRNAsong: a web-based tool for generation and testing of miRNA sponge constructs in silico

MicroRNA (miRNA) sponges are RNA transcripts containing multiple high-affinity binding sites that associate with and sequester specific miRNAs to prevent them from interacting with their target messenger (m)RNAs. Due to the high specificity of miRNA sponges and strong inhibition of target miRNAs, these molecules have become increasingly applied in miRNA loss-of-function studies. However, improperly designed sponge constructs may sequester off-target miRNAs; thus, it has become increasingly important to develop a tool for miRNA sponge construct design and testing. In this study, we introduce microRNA sponge generator and tester (miRNAsong), a freely available web-based tool for generation and in silico testing of miRNA sponges. This tool generates miRNA sponge constructs for specific miRNAs and miRNA families/clusters and tests them for potential binding to miRNAs in selected organisms. Currently, miRNAsong allows for testing of sponge constructs in 219 species covering 35,828 miRNA sequences. Furthermore, we also provide an example, supplemented with experimental data, of how to use this tool. Using miRNAsong, we designed and tested a sponge for miR-145 inhibition, and cloned the sequence into an inducible lentiviral vector. We found that established cell lines expressing miR-145 sponge strongly inhibited miR-145, thus demonstrating the usability of miRNAsong tool for sponge generation. URL: http://www.med.muni.cz/histology/miRNAsong/.

Investigating miRNA-mRNA regulatory networks using crosslinking immunoprecipitation methods for biomarker and target discovery in cancer

INTRODUCTION

MicroRNAs (miRNAs) are small non-coding RNAs that regulate gene expression at the post-transcriptional level. Recently, different experimental approaches, such as RNA Sequencing, crosslinking immunoprecipitation (CLIP) methods and its variations, together with computational approaches have been developed to elucidate the miRNA-mRNA targetome. Areas covered: This report focuses on comparing the different experimental and computational approaches, describing their advantages and disadvantages and providing several examples of preclinical (in vitro and in vivo) and clinical studies that have identified miRNA target genes in various tumour types, including breast, ovary, colorectal and pancreas. Expert commentary: The combination of CLIP methods with bioinformatic analyses is essential to better predict miRNA-mRNA interactions and associate their specific pathways within the extensive regulatory network. Nevertheless, further studies are needed to overcome the difficulties these methods have, in order to find a gold standard method that identifies, without any bias, the regulatory association between miRNAs and their target mRNAs.

LncRNA ontology: inferring lncRNA functions based on chromatin states and expression patterns

Accumulating evidences suggest that long non-coding RNAs (lncRNAs) perform important functions. Genome-wide chromatin-states area rich source of information about cellular state, yielding insights beyond what is typically obtained by transcriptome profiling. We propose an integrative method for genome-wide functional predictions of lncRNAs by combining chromatin states data with gene expression patterns. We first validated the method using protein-coding genes with known function annotations. Our validation results indicated that our integrative method performs better than co-expression analysis, and is accurate across different conditions. Next, by applying the integrative model genome-wide, we predicted the probable functions for more than 97% of human lncRNAs. The putative functions inferred by our method match with previously annotated by the targets of lncRNAs. Moreover, the linkage from the cellular processes influenced by cancer-associated lncRNAs to the cancer hallmarks provided a “lncRNA point-of-view” on tumor biology. Our approach provides a functional annotation of the lncRNAs, which we developed into a web-based application, LncRNA Ontology, to provide visualization, analysis, and downloading of lncRNA putative functions.

Learning to Predict miRNA-mRNA Interactions from AGO CLIP Sequencing and CLASH Data

Recent technologies like AGO CLIP sequencing and CLASH enable direct transcriptome-wide identification of AGO binding and miRNA target sites, but the most widely used miRNA target prediction algorithms do not exploit these data. Here we use discriminative learning on AGO CLIP and CLASH interactions to train a novel miRNA target prediction model. Our method combines two SVM classifiers, one to predict miRNA-mRNA duplexes and a second to learn a binding model of AGO’s local UTR sequence preferences and positional bias in 3’UTR isoforms. The duplex SVM model enables the prediction of non-canonical target sites and more accurately resolves miRNA interactions from AGO CLIP data than previous methods. The binding model is trained using a multi-task strategy to learn context-specific and common AGO sequence preferences. The duplex and common AGO binding models together outperform existing miRNA target prediction algorithms on held-out binding data. Open source code is available at https://bitbucket.org/leslielab/chimiric.

MicroRNAs (or miRNAs) are a family of small RNA molecules that guide Argonaute (AGO) to specific target sites within mRNAs and regulate numerous biological processes in normal cells and in disease. Despite years of research, the principles of miRNA targeting are incompletely understood, and computational miRNA target prediction methods still achieve only modest performance. Most previous target prediction work has been based on indirect measurements of miRNA regulation, such as mRNA expression changes upon miRNA perturbation, without mapping actual binding sites, which limits accuracy and precludes discovery of more subtle miRNA targeting rules. The recent introduction of CLIP (UV crosslinking followed by immunoprecipitation) sequencing technologies enables direct identification of interactions between miRNAs and mRNAs. However, the data generated from these assays has not been fully exploited in target prediction. Here, we present a model to predict miRNA-mRNA interactions solely based on their sequences, using new technologies to map AGO and miRNA binding interactions with machine learning techniques. Our algorithm produces more accurate predictions than state-of-the-art methods based on indirect measurements. Moreover, interpretation of the learned model reveals novel features of miRNA-mRNA interactions, including potential cooperativity with specific RNA-binding proteins.

Circular RNA of the human sphingomyelin synthase 1 gene: Multiple splice variants, evolutionary conservatism and expression in different tissues

The human sphingomyelin synthase 1 gene (SGMS1) encodes an essential enzyme that is involved in the synthesis of sphingomyelin and diacylglycerol from phosphatidylcholine and ceramide. Among the products of SGMS1, we found new transcripts, circular RNAs (circRNAs), that contain sequences of the gene's 5' untranslated region (5'UTR). Some of them include the gene's coding region and fragments of introns. An analysis of the abundance of circRNAs in human tissues showed that the largest transcripts were predominantly found in different parts of the brain. circRNAs of rat and mouse sphingomyelin synthase 1 orthologous genes were detected and are highly similar to the human SGMS1 gene transcripts. A quantitative analysis of the abundance of such transcripts also revealed their elevated amount in the brain. A computational analysis of sequences of human circRNAs showed their high potential of binding microRNAs (miRNAs), including the miRNAs that form complexes with Ago proteins and the mRNA of SGMS1. We assume that the circRNAs identified here participate in the regulation of the function of the SGMS1 gene in the brain.

miRepress: modelling gene expression regulation by microRNA with non-conventional binding sites

Some earlier studies have reported an alternative mode of microRNA-target interaction. We detected target regions within mRNA transcripts from AGO PAR-CLIP that did not contain any conventional microRNA seed pairing but only had non-conventional binding sites with microRNA 3' end. Our study from 7 set of data that measured global protein fold change after microRNA transfection pointed towards the association of target protein fold change with 6-mer and 7-mer target sites involving microRNA 3' end. We developed a model to predict the degree of microRNA target regulation in terms of protein fold changes from the number of different conventional and non-conventional target sites present in the target, and found significant correlation of its output with protein expression changes. We validated the effect of non-conventional interactions with target by modulating the abundance of microRNA in a human breast cancer cell line MCF-7. The validation was done using luciferase assay and immunoblot analysis for our predicted non-conventional microRNA-target pair WNT1 (3' UTR) and miR-367-5p and immunoblot analysis for another predicted non-conventional microRNA-target pair MYH10 (coding region) and miR-181a-5p. Both experiments showed inhibition of targets by transfection of microRNA mimics that were predicted to have only non-conventional sites.