Current experimental strategies for intracellular target identification of microRNA

A snapshot of miRNAs in oral squamous cell carcinoma: Difference between cancer cells and corresponding normal cells

Oral squamous cell carcinoma (OSCC) constitutes the most aggressive tumors of the oral cavity and is one of the leading causes of cancer mortality worldwide. Although recent clinical treatment strategies have improved the survival rate, the outcome of OSCC patients still remains dismal because of the lack of efficient diagnostic and treatment tools. As one of the main actors of OSCC scenario, microRNAs (miRNAs) are involved in triggering, progression and metastasis through the regulation of various cancer-related signaling pathways. Identification followed by precise study of the biology and mechanism of action of miRNAs will greatly help to provide valuable insights regarding OSCC development and can be considered as an anti-OSCC target. In the current review, we have provided a focused summary of the latest published papers on the role of miRNAs in apoptosis, cell cycle, proliferation, EMT and metastasis of OSCC as well as the role of long noncoding RNAs in the modulation of miRNAs in OSCC.

MicroRNA-Target Identification: A Combinatorial In Silico Approach

Contemporary computational target prediction tools with their distinctive properties and stringency have been playing a vital role in pursuing putative targets for a solitary miRNA or a subcategory of miRNAs. These tools utilize a defined set of probabilistic algorithms, machine learning techniques, and information of experimentally validated miRNA targets to provide the best selection. However, there are numerous false-positive predictions, and a method to choose an archetypal approach and put the data provided by the prediction tools into context is still lacking. Moreover, sensitivity, specificity, and overall efficiency of a single tool have not yet been achieved. Therefore, a systematic combination of selective online tools combining elementary and advanced factors of miRNA target identification might reinforce the current target prediction regime. The focus of this study was to build a comprehensive workflow by combining six available online tools to facilitate the current understanding of miRNA-target prediction.

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.

mintRULS: Prediction of miRNA–mRNA Target Site Interactions Using Regularized Least Square Method

Identification of miRNA–mRNA interactions is critical to understand the new paradigms in gene regulation. Existing methods show suboptimal performance owing to inappropriate feature selection and limited integration of intuitive biological features of both miRNAs and mRNAs. The present regularized least square-based method, mintRULS, employs features of miRNAs and their target sites using pairwise similarity metrics based on free energy, sequence and repeat identities, and target site accessibility to predict miRNA-target site interactions. We hypothesized that miRNAs sharing similar structural and functional features are more likely to target the same mRNA, and conversely, mRNAs with similar features can be targeted by the same miRNA. Our prediction model achieved an impressive AUC of 0.93 and 0.92 in LOOCV and LmiTOCV settings, respectively. In comparison, other popular tools such as miRDB, TargetScan, MBSTAR, RPmirDIP, and STarMir scored AUCs at 0.73, 0.77, 0.55, 0.84, and 0.67, respectively, in LOOCV setting. Similarly, mintRULS outperformed other methods using metrics such as accuracy, sensitivity, specificity, and MCC. Our method also demonstrated high accuracy when validated against experimentally derived data from condition- and cell-specific studies and expression studies of miRNAs and target genes, both in human and mouse.

Implication of genetic variants in primary microRNA processing sites in the risk of multiple sclerosis

Background

Multiple sclerosis (MS) is a chronic inflammatory disease of the central nervous system with a well-established genetic contribution to susceptibility. Over 200 genetic regions have been linked to the inherited risk of developing MS, but the disease-causing variants and their functional effects at the molecular level are still largely unresolved. We hypothesised that MS-associated single-nucleotide polymorphisms (SNPs) affect the recognition and enzymatic cleavage of primary microRNAs (pri-miRNAs).

Methods

Our study focused on 11 pri-miRNAs (9 primate-specific) that are encoded in genetic risk loci for MS. The levels of mature miRNAs and potential isoforms (isomiRs) produced from those pri-miRNAs were measured in B cells obtained from the peripheral blood of 63 MS patients and 28 healthy controls. We tested for associations between SNP genotypes and miRNA expression in cis using quantitative trait locus (cis-miR-eQTL) analyses. Genetic effects on miRNA stem-loop processing efficiency were verified using luciferase reporter assays. Potential direct miRNA target genes were identified by transcriptome profiling and computational binding site assessment.

Findings

Mature miRNAs and isomiRs from hsa-mir-26a-2, hsa-mir-199a-1, hsa-mir-4304, hsa-mir-4423, hsa-mir-4464 and hsa-mir-4492 could be detected in all B-cell samples. When MS patient subgroups were compared with healthy controls, a significant differential expression was observed for miRNAs from the 5’ and 3’ strands of hsa-mir-26a-2 and hsa-mir-199a-1. The cis-miR-eQTL analyses and reporter assays pointed to a slightly more efficient Drosha-mediated processing of hsa-mir-199a-1 when the MS risk allele T of SNP rs1005039 is present. On the other hand, the MS risk allele A of SNP rs817478, which substitutes the first C in a CNNC sequence motif, was found to cause a markedly lower efficiency in the processing of hsa-mir-4423. Overexpression of hsa-mir-199a-1 inhibited the expression of 60 protein-coding genes, including IRAK2, MIF, TNFRSF12A and TRAF1. The only target gene identified for hsa-mir-4423 was TMEM47.

Interpretation

We found that MS-associated SNPs in sequence determinants of pri-miRNA processing can affect the expression of mature miRNAs. Our findings complement the existing literature on the dysregulation of miRNAs in MS. Further studies on the maturation and function of miRNAs in different cell types and tissues may help to gain a more detailed functional understanding of the genetic basis of MS.

Funding

This study was funded by the Rostock University Medical Center (FORUN program, grant: 889002), Sanofi Genzyme (grant: GZ-2016-11560) and Merck Serono GmbH (Darmstadt, Germany, an affiliate of Merck KGaA, CrossRef Funder ID: 10.13039/100009945, grant: 4501860307). NB was supported by the Stiftung der Deutschen Wirtschaft (sdw) and the FAZIT foundation. EP was supported by the Landesgraduiertenförderung Mecklenburg-Vorpommern.

Regulatory role of microRNAs (miRNAs) in the recent development of abiotic stress tolerance of plants

MicroRNAs (miRNAs) are a distinct groups of single-stranded non-coding, tiny regulatory RNAs approximately 20-24 nucleotides in length. miRNAs negatively influence gene expression at the post-transcriptional level and have evolved considerably in the development of abiotic stress tolerance in a number of model plants and economically important crop species. The present review aims to deliver the information on miRNA-mediated regulation of the expression of major genes or Transcription Factors (TFs), as well as genetic and regulatory pathways. Also, the information on adaptive mechanisms involved in plant abiotic stress responses, prediction, and validation of targets, computational tools, and databases available for plant miRNAs, specifically focus on their exploration for engineering abiotic stress tolerance in plants. The regulatory function of miRNAs in plant growth, development, and abiotic stresses consider in this review, which uses high-throughput sequencing (HTS) technologies to generate large-scale libraries of small RNAs (sRNAs) for conventional screening of known and novel abiotic stress-responsive miRNAs adds complexity to regulatory networks in plants. The discoveries of miRNA-mediated tolerance to multiple abiotic stresses, including salinity, drought, cold, heat stress, nutritional deficiency, UV-radiation, oxidative stress, hypoxia, and heavy metal toxicity, are highlighted and discussed in this review.

Analysing miRNA-Target Gene Networks in Inflammatory Bowel Disease and Other Complex Diseases Using Transcriptomic Data

Patients with inflammatory bowel disease (IBD) are known to have perturbations in microRNA (miRNA) levels as well as altered miRNA regulation. Although experimental methods have provided initial insights into the functional consequences that may arise due to these changes, researchers are increasingly utilising novel bioinformatics approaches to further dissect the role of miRNAs in IBD. The recent exponential increase in transcriptomics datasets provides an excellent opportunity to further explore the role of miRNAs in IBD pathogenesis. To effectively understand miRNA-target gene interactions from gene expression data, multiple database resources are required, which have become available in recent years. In this technical note, we provide a step-by-step protocol for utilising these state-of-the-art resources, as well as systems biology approaches to understand the role of miRNAs in complex disease pathogenesis. We demonstrate through a case study example how to combine the resulting miRNA-target gene networks with transcriptomics data to find potential disease-specific miRNA regulators and miRNA-target genes in Crohn’s disease. This approach could help to identify miRNAs that may have important disease-modifying effects in IBD and other complex disorders, and facilitate the discovery of novel therapeutic targets.

MicroRNA Targeting

MicroRNAs (miRNAs) are small noncoding elements that play essential roles in the posttranscriptional regulation of biochemical processes. miRNAs recognize and target multiple mRNAs; therefore, investigating miRNA dysregulation is an indispensable strategy to understand pathological conditions and to design innovative drugs. Targeting miRNAs in diseases improve outcomes of several therapeutic strategies thus, this present study highlights miRNA targeting methods through experimental assays and bioinformatics tools. The first part of this review focuses on experimental miRNA targeting approaches for elucidating key biochemical pathways. A growing body of evidence about the miRNA world reveals the fact that it is not possible to uncover these molecules' structural and functional characteristics related to the biological processes with a deterministic approach. Instead, a systemic point of view is needed to truly understand the facts behind the natural complexity of interactions and regulations that miRNA regulations present. This task heavily depends both on computational and experimental capabilities. Fortunately, several miRNA bioinformatics tools catering to nonexperts are available as complementary wet-lab approaches. For this purpose, this work provides recent research and information about computational tools for miRNA targeting research.

Plant RNA-mediated gene regulatory network

Not all transcribed RNAs are protein-coding RNAs. Many of them are non-protein-coding RNAs in diverse eukaryotes. However, some of them seem to be non-functional and are resulted from spurious transcription. A lot of non-protein-coding transcripts have a significant function in the translation process. Gene expressions depend on complex networks of diverse gene regulatory pathways. Several non-protein-coding RNAs regulate gene expression in a sequence-specific system either at the transcriptional level or post-transcriptional level. They include a significant part of the gene expression regulatory network. RNA-mediated gene regulation machinery is evolutionarily ancient. They well-evolved during the evolutionary time and are becoming much more complex than had been expected. In this review, we are trying to summarizing the current knowledge in the field of RNA-mediated gene silencing.

MicroRNA regulation of critical retinal pigment epithelial functions

MicroRNAs are short, evolutionarily conserved noncoding RNAs that are critical for the control of normal cellular physiology. In the retina, photoreceptors are highly specialized neurons that transduce light into electrical signals. Photoreceptors, however, are unable to process visual stimuli without the support of the retinal pigment epithelium (RPE). The RPE performs numerous functions to aid the retina, including the generation of visual chromophore and metabolic support. Recent work has underscored how microRNAs enable vision through their contributions to RPE functions. This review focuses on the biogenesis and control of microRNAs in rodents and humans, the roles microRNAs play in RPE function and degeneration, and how microRNAs could serve as potential therapeutics and biomarkers for visual diseases.

MicroRNA Interactome Multiomics Characterization for Cancer Research and Personalized Medicine: An Expert Review

MicroRNAs (miRNAs) that are mutually modulated by their interacting partners (interactome) are being increasingly noted for their significant role in pathogenesis and treatment of various human cancers. Recently, miRNA interactome dissected with multiomics approaches has been the subject of focus since individual tools or methods failed to provide the necessary comprehensive clues on the complete interactome. Even though single-omics technologies such as proteomics can uncover part of the interactome, the biological and clinical understanding still remain incomplete. In this study, we present an expert review of studies involving multiomics approaches to identification of miRNA interactome and its application in mechanistic characterization, classification, and therapeutic target identification in a variety of cancers, and with a focus on proteomics. We also discuss individual or multiple miRNA-based interactome identification in various pathological conditions of relevance to clinical medicine. Various new single-omics methods that can be integrated into multiomics cancer research and the computational approaches to analyze and predict miRNA interactome are also highlighted in this review. In all, we contextulize the power of multiomics approaches and the importance of the miRNA interactome to achieve the vision and practice of predictive, preventive, and personalized medicine in cancer research and clinical oncology.

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 .

A combinatorial in silico approach for microRNA-target identification: Order out of chaos

Contemporary computational microRNA(miRNA)-target prediction tools have been playing a vital role in pursuing putative targets for a solitary miRNA or a group of miRNAs. These tools utilise a set of probabilistic algorithms, machine learning techniques and analyse experimentally validated miRNA targets to identify the potential miRNA-target pairs. Unfortunately, current tools generate a huge number of false-positive predictions. A standardized approach with a single tool or a combination of tools is still lacking. Moreover, sensitivity, specificity and overall efficiency of any single tool are yet to be satisfactory. Therefore, a systematic combination of selective online tools combining the factors regarding miRNA-target identification would be valuable as an miRNA-target prediction scheme. The focus of this study was to develop a theoretical framework by combining six available online tools to facilitate the current understanding of miRNA-target identification.

Dysregulation of miR-638 in the progression of cancers

MicroRNA (miRNA) is a form of short, single-stranded and non-coding RNA that is important in regulating the post-transcriptional modification of multiple downstream targets. Many miRNAs have been reported to involve in controlling the progression of human diseases, and one of them is miR-638, which play essential roles in regulating the development of human cancer. By targeting the 3'-ends of its targets, miR-638 can regulate cellular processes including proliferation, invasion, metastases, angiogenesis, apoptosis and inflammation. This review was aimed to summarize current findings on the roles of miR-638 in different human cancers based on the results from various in vitro, in vivo and clinical studies. The biogenesis process and tissue expression, followed by the roles of miR-638 in regulating the development of various human cancers by targeting different downstream targets were covered in this review. The potential applications and challenges of employing miR-638 as cancer biomarker and therapeutic agent were also discussed.

Integrative approaches for analysis of mRNA and microRNA high-throughput data

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Review on tools and databases linking miRNA and its mRNA targetome.

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Databases show little overlap in miRNA targetome predictions suggesting strong contextual effects.

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Deconvolution and deep learning approaches are promising new approaches to improve miRNA targetome predictions.

Advanced sequencing technologies such as RNASeq provide the means for production of massive amounts of data, including transcriptome-wide expression levels of coding RNAs (mRNAs) and non-coding RNAs such as miRNAs, lncRNAs, piRNAs and many other RNA species. In silico analysis of datasets, representing only one RNA species is well established and a variety of tools and pipelines are available. However, attaining a more systematic view of how different players come together to regulate the expression of a gene or a group of genes requires a more intricate approach to data analysis. To fully understand complex transcriptional networks, datasets representing different RNA species need to be integrated. In this review, we will focus on miRNAs as key post-transcriptional regulators summarizing current computational approaches for miRNA:target gene prediction as well as new data-driven methods to tackle the problem of comprehensively and accurately dissecting miRNome-targetome interactions.

Transcriptional and post-transcriptional regulation of the pregnane X receptor: a rationale for interindividual variability in drug metabolism

The pregnane X receptor (PXR, encoded by the NR1I2 gene) is a ligand-regulated transcription factor originally described as a master regulator of xenobiotic detoxification. Later, however, PXR was also shown to interact with endogenous metabolism and to be further associated with various pathological states. This review focuses predominantly on such aspects, currently less covered in literature, as the control of PXR expression per se in the context of inter-individual differences in drug metabolism. There is growing evidence that non-coding RNAs post-transcriptionally regulate PXR. Effects on PXR have especially been reported for microRNAs (miRNAs), which include miR-148a, miR-18a-5p, miR-140-3p, miR-30c-1-3p and miR-877-5p. Likewise, miRNAs control the expression of both transcription factors involved in PXR expression and regulators of PXR function. The impact of NR1I2 genetic polymorphisms on miRNA-mediated PXR regulation is also discussed. As revealed recently, long non-coding RNAs (lncRNAs) appear to interfere with PXR expression. Reciprocally, PXR activation regulates non-coding RNA expression, thus comprising another level of PXR action in addition to the direct transactivation of protein-coding genes. PXR expression is further controlled by several transcription factors (cross-regulation) giving rise to different PXR transcript variants. Controversies remain regarding the suggested role of feedback regulation (auto-regulation) of PXR expression. In this review, we comprehensively summarize the miRNA-mediated, lncRNA-mediated and transcriptional regulation of PXR expression, and we propose that deciphering the precise mechanisms of PXR expression may bridge our knowledge gap in inter-individual differences in drug metabolism and toxicity.

Key MicroRNA's and Their Targetome in Adrenocortical Cancer

Adrenocortical Carcinoma (ACC) is a rare but aggressive malignancy with poor prognosis and limited response to available systemic therapies. Although complete surgical resection gives the best chance for long-term survival, ACC has a two-year recurrence rate of 50%, which poses a therapeutic challenge. High throughput analyses focused on characterizing the molecular signature of ACC have revealed specific micro-RNAs (miRNAs) that are associated with aggressive tumor phenotypes. MiRNAs are small non-coding RNA molecules that regulate gene expression by inhibiting mRNA translation or degrading mRNA transcripts and have been generally implicated in carcinogenesis. This review summarizes the current insights into dysregulated miRNAs in ACC tumorigenesis, their known functions, and specific targetomes. In addition, we explore the possibility of particular miRNAs to be exploited as clinical biomarkers in ACC and as potential therapeutics.

A photoactivatable microRNA probe for identification of microRNA targets and light-controlled suppression of microRNA target expression

Here, we report a novel dual-functional microRNA (miRNA) probe, PA-miRNA, for miRNA target identification and light control of miRNA target expression. PA-miRNA is a miRNA mimic with a 3'-biotin tag linked via a photo-cleavable linker. Using PA-miR-34a, intracellular targets of miR-34a in HeLa cells were isolated and confirmed. Moreover, PA-miR-34a upon transfection into HeLa cells was inactive until light irradiation to break the photo-cleavable linker to release functional miR-34a. We demonstrated that miR-34a target expression as well as miR-34a-promoted cell apoptosis were regulated by PA-miR-34a in a photo-controllable manner.