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

Integrative in silico approaches to analyse microRNA-mediated responses in human diseases

Advancements in sequencing technologies have facilitated omics level information generation for various diseases in human. High-throughput technologies have become a powerful tool to understand differential expression studies and transcriptional network analysis. An understanding of complex transcriptional networks in human diseases requires integration of datasets representing different RNA species including microRNA (miRNA) and messenger RNA (mRNA). This review emphasises on conceptual explanation of generalized workflow and methodologies to the miRNA mediated responses in human diseases by using different in silico analysis. Although, there have been many prior explorations in miRNA-mediated responses in human diseases, the advantages, limitations and overcoming the limitation through different statistical techniques have not yet been discussed. This review focuses on miRNAs as important gene regulators in human diseases, methodologies for miRNA-target gene prediction and data driven methods for enrichment and network analysis for miRnome-targetome interactions. Additionally, it proposes an integrative workflow to analyse structural components of networks obtained from high-throughput data. This review explains how to apply the existing methods to analyse miRNA-mediated responses in human diseases. It addresses unique characteristics of different analysis, its limitations and its statistical solutions influencing the choice of methods for the analysis through a workflow. Moreover, it provides an overview of promising common integrative approaches to comprehend miRNA-mediated gene regulatory events in biological processes in humans. The proposed methodologies and workflow shall help in the analysis of multi-source data to identify molecular signatures of various human diseases.

Empowering prediction of miRNA-mRNA interactions in species with limited training data through transfer learning

MicroRNAs (miRNAs) play a crucial role in mRNA regulation. Identifying functionally important mRNA targets of a specific miRNA is essential for uncovering its biological function and assisting miRNA-based drug development. Datasets of high-throughput direct bona fide miRNA-target interactions (MTIs) exist only for a few model organisms, prompting the need for computational prediction. However, the scarcity of data poses a challenge in training accurate machine learning models for MTI prediction. In this study, we explored the potential of transfer learning technique (with ANN and XGB) to address the limited data challenge by leveraging the similarities in interaction rules between species. Furthermore, we introduced a novel approach called TransferSHAP for estimating the feature importance of transfer learning in tabular dataset tasks. We demonstrated that transfer learning improves MTI prediction accuracy for species with limited datasets and identified the specific interaction features the models employed to transfer information across different species.

When Argonaute takes out the ribonuclease sword

Argonaute (AGO) proteins in all three domains of life form ribonucleoprotein or deoxyribonucleoprotein complexes by loading a guide RNA or DNA, respectively. Since all AGOs retain a PIWI domain that takes an RNase H fold, the ancestor was likely an endoribonuclease (i.e., a slicer). In animals, most miRNA-mediated gene silencing occurs slicer independently. However, the slicer activity of AGO is indispensable in specific events, such as development and differentiation, which are critical for vertebrates and thus cannot be replaced by the slicer-independent regulation. This review highlights the distinctions in catalytic activation mechanisms among slicing-competent AGOs, shedding light on the roles of two metal ions in target recognition and cleavage. The precision of the target specificity by the RNA-induced silencing complexes is reevaluated and redefined. The possible coevolutionary relationship between slicer-independent gene regulation and AGO-binding protein, GW182, is also explored. These discussions reveal that numerous captivating questions remain unanswered regarding the timing and manner in which AGOs employ their slicing activity.

Molecular Mediators and Regulators of Retinal Angiogenesis

BACKGROUND

Retinal neovascularization is the major cause of vision loss that affects both adults and young children including premature babies. It has been a major pathology in several retinal diseases like age-related macular degeneration (AMD), diabetic retinopathy (DR) and retinopathy of prematurity (ROP). Current treatment modalities such as anti-VEGF therapy, laser are not suitable for every patient and response to these therapies is highly variable. Thus, there is a need to investigate newer therapeutic targets for DR, ROP and AMD, based on a clear understanding of disease pathology and regulatory mechanisms involved.

METHOD

Appropriate articles published till February 2021 were extracted from PUBMED using keywords like ocular angiogenesis, DR, ROP, AMD, miRNA, mRNA, and cirMiRNA and containvaluable information regarding the involvement of miRNA in causing neovascularization. After compiling the list of miRNA regulating mRNA expression in angiogenesis and neovascularaization, their interactions were studied using online available tool MIENTURNET (http://userver.bio.uniroma1.it/apps/mienturnet/). The pathways involved in these processes were also predicted using the same tool.

RESULTS

Most of the studies have explored potential targets like HIF1-α, PDGF, TGFβ, FGF, etc., for their involvement in pathological angiogenesis in different retinal diseases. The regulatory role of microRNA (miRNA) has also been explored in various retinal ocular pathologies. This review highlights regulatory mechanism of cellular and circulatory miRNAs and their interactions with the genes involved in retinal neovascularization. The role of long noncoding RNA (ncRNA) in the regulation of genes involved in different pathways is also noteworthy and discussed in this review.

CONCLUSION

This review highlights the potential regulatory mechanism/pathways involved in retinal neovascularization and its implications in retinal diseases and for identifying new drug targets.

Overview of Computational and Experimental Methods to Identify Tissue-Specific MicroRNA Targets

As ubiquitous posttranscriptional regulators of gene expression, microRNAs (miRNAs) play key roles in cell physiology and function across taxa. In the last two decades, we have gained a good understanding about miRNA biogenesis pathways, modes of action, and consequences of miRNA-mediated gene regulation. More recently, research has focused on exploring causes for miRNA dysregulation, miRNA-mediated crosstalk between genes and signaling pathways, and the role of miRNAs in disease.This chapter discusses methods for the identification of miRNA-target interactions and causes for tissue-specific miRNA-target regulation. Computational approaches for predicting miRNA target sites and assessing tissue-specific target regulation are discussed. Moreover, there is an emphasis on features that affect miRNA target recognition and how high-throughput sequencing protocols can help in assessing miRNA-mediated gene regulation on a genome-wide scale. In addition, this chapter introduces some experimental approaches for the validation of miRNA targets as well as web-based resources sharing predicted and validated miRNA-target interactions.

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.

Molecular evolution and functional modification of plant miRNAs with CRISPR

Gene editing using clustered regularly interspaced short palindromic repeat/CRISPR-associated proteins (CRISPR/Cas) has revolutionized biotechnology and provides genetic tools for medicine and life sciences. However, the application of this technology to miRNAs, with the function as negative gene regulators, has not been extensively reviewed in plants. Here, we summarize the evolution, biogenesis, and structure of miRNAs, as well as their interactions with mRNAs and computational models for predicting target genes. In addition, we review current advances in CRISPR/Cas for functional analysis and for modulating miRNA genes in plants. Extending our knowledge of miRNAs and their manipulation with CRISPR will provide fundamental understanding of the functions of plant miRNAs and facilitate more sustainable and publicly acceptable genetic engineering of crops.

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

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

miRNA Combinatorics and its Role in Cell State Control-A Probabilistic Approach

A hallmark of cancer evolution is that the tumor may change its cell identity and improve its survival and fitness. Drastic change in microRNA (miRNA) composition and quantities accompany such dynamic processes. Cancer samples are composed of cells’ mixtures of varying stages of cancerous progress. Therefore, cell-specific molecular profiling represents cellular averaging. In this study, we consider the degree to which altering miRNAs composition shifts cell behavior. We used COMICS, an iterative framework that simulates the stochastic events of miRNA-mRNA pairing, using a probabilistic approach. COMICS simulates the likelihood that cells change their transcriptome following many iterations (100 k). Results of COMICS from the human cell line (HeLa) confirmed that most genes are resistant to miRNA regulation. However, COMICS results suggest that the composition of the abundant miRNAs dictates the nature of the cells (across three cell lines) regardless of its actual mRNA steady-state. In silico perturbations of cell lines (i.e., by overexpressing miRNAs) allowed to classify genes according to their sensitivity and resilience to any combination of miRNA perturbations. Our results expose an overlooked quantitative dimension for a set of genes and miRNA regulation in living cells. The immediate implication is that even relatively modest overexpression of specific miRNAs may shift cell identity and impact cancer evolution.

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

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

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 .

Cross-Linking Ligation and Sequencing of Hybrids (qCLASH) Reveals an Unpredicted miRNA Targetome in Melanoma Cells

MicroRNAs are key post-transcriptional gene regulators often displaying aberrant expression patterns in cancer. As microRNAs are promising disease-associated biomarkers and modulators of responsiveness to anti-cancer therapies, a solid understanding of their targetome is crucial. Despite enormous research efforts, the success rates of available tools to reliably predict microRNAs (miRNA)-target interactions remains limited. To investigate the disease-associated miRNA targetome, we have applied modified cross-linking ligation and sequencing of hybrids (qCLASH) to BRAF-mutant melanoma cells. The resulting RNA-RNA hybrid molecules provide a comprehensive and unbiased snapshot of direct miRNA-target interactions. The regulatory effects on selected miRNA target genes in predicted vs. non-predicted binding regions was validated by miRNA mimic experiments. Most miRNA-target interactions deviate from the central dogma of miRNA targeting up to 60% interactions occur via non-canonical seed pairing with a strong contribution of the 3' miRNA sequence, and over 50% display a clear bias towards the coding sequence of mRNAs. miRNAs targeting the coding sequence can directly reduce gene expression (miR-34a/CD68), while the majority of non-canonical miRNA interactions appear to have roles beyond target gene suppression (miR-100/AXL). Additionally, non-mRNA targets of miRNAs (lncRNAs) whose interactions mainly occur via non-canonical binding were identified in melanoma. This first application of CLASH sequencing to cancer cells identified over 8 K distinct miRNA-target interactions in melanoma cells. Our data highlight the importance non-canonical interactions, revealing further layers of complexity of post-transcriptional gene regulation in melanoma, thus expanding the pool of miRNA-target interactions, which have so far been omitted in the cancer field.

MicroRNAs and Their Targetomes in Tumor-Immune Communication

The development of cancer is a complex and dynamically regulated multiple-step process that involves many changes in gene expression. Over the last decade, microRNAs (miRNAs), a class of short regulatory non-coding RNAs, have emerged as key molecular effectors and regulators of tumorigenesis. While aberrant expression of miRNAs or dysregulated miRNA-mediated gene regulation in tumor cells have been shown to be capable of directly promoting or inhibiting tumorigenesis, considering the well-reported role of the immune system in cancer, tumor-derived miRNAs could also impact tumor growth through regulating anti-tumor immune responses. Here, we discuss howmiRNAs can function as central mediators that influence the crosstalk between cancer and the immune system. Moreover, we also review the current progress in the development of novel experimental approaches for miRNA target identification that will facilitate our understanding of miRNA-mediated gene regulation in not only human malignancies, but also in other genetic disorders.

Position-wise binding preference is important for miRNA target site prediction

MOTIVATION

It is a fundamental task to identify microRNAs (miRNAs) targets and accurately locate their target sites. Genome-scale experiments for miRNA target site detection are still costly. The prediction accuracies of existing computational algorithms and tools are often not up to the expectation due to a large number of false positives. One major obstacle to achieve a higher accuracy is the lack of knowledge of the target binding features of miRNAs. The published high-throughput experimental data provide an opportunity to analyze position-wise preference of miRNAs in terms of target binding, which can be an important feature in miRNA target prediction algorithms.

RESULTS

We developed a Markov model to characterize position-wise pairing patterns of miRNA-target interactions. We further integrated this model as a scoring method and developed a dynamic programming (DP) algorithm, MDPS (Markov model-scored Dynamic Programming algorithm for miRNA target site Selection) that can screen putative target sites of miRNA-target binding. The MDPS algorithm thus can take into account both the dependency of neighboring pairing positions and the global pairing information. Based on the trained Markov models from both miRNA-specific and general datasets, we discovered that the position-wise binding information specific to a given miRNA would benefit its target prediction. We also found that miRNAs maintain region-wise similarity in their target binding patterns. Combining MDPS with existing methods significantly improves their precision while only slightly reduces their recall. Therefore, position-wise pairing patterns have the promise to improve target prediction if incorporated into existing software tools.

AVAILABILITY AND IMPLEMENTATION

The source code and tool to calculate MDPS score is available at http://hulab.ucf.edu/research/projects/MDPS/index.html.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Comprehensive Analysis of Human microRNA-mRNA Interactome

MicroRNAs play a key role in the regulation of gene expression. A majority of microRNA–mRNA interactions remain unidentified. Despite extensive research, our ability to predict human microRNA-mRNA interactions using computational algorithms remains limited by a complexity of the models for non-canonical interactions, and an abundance of false-positive results. Here, we present the landscape of human microRNA–mRNA interactions derived from comprehensive analysis of HEK293 and Huh7.5 datasets, along with publicly available microRNA and mRNA expression data. We show that, while only 1–2% of human genes were the most regulated by microRNAs, few cell line–specific RNAs, including EEF1A1 and HSPA1B in HEK293 and AFP, APOB, and MALAT1 genes in Huh7.5, display substantial “sponge-like” properties. We revealed a group of microRNAs that are expressed at a very high level, while interacting with only a few mRNAs, which, indeed, serve as their specific expression regulators. In order to establish reliable microRNA-binding regions, we collected and systematically analyzed the data from 79 CLIP datasets of microRNA-binding sites. We report 46,805 experimentally confirmed mRNA–miRNA duplex regions. Resulting dataset is available at http://score.generesearch.ru/services/mirna/. Our study provides initial insight into the complexity of human microRNA–mRNA interactions.

MicroRNA Assisted Gene Regulation in Colorectal Cancer

Colorectal cancer (CRC) is the second-leading cause of cancer death and a major public health problem. Nearly 80% CRC cases are diagnosed after the disease have metastasized and are often too advanced for treatment. Small non-coding RNA guides argonaute protein to their specific target for regulation as the sole of RNA induced silencing complex for gene silencing. These non-coding RNA for example microRNA, are thought to play a key role in affecting the efficiency of gene regulation in cancer, especially CRC. Understanding the mechanism at the molecular level could lead to improved diagnosis, treatment, and management decisions for CRC. The study aimed to predict the molecular mechanism of gene regulation based microRNA-mRNA duplex as a lead in the silencing mechanism. Five candidate microRNAs were identified through the in silico approach. The MicroRNA target prediction and subsequent correlation, and prioritization were performed using miRTarBase, gbCRC and CoReCG, and DAVID databases respectively. Protein selection and preparation were carried out using PDB and Schrödinger suits. The molecular docking analysis was performed using PATCHDOCK webserver and visualized by discovery studio visualizer. The results of the study reveal that the candidate microRNAs have strong binding affinity towards their targets suggesting a crucial factor in the silencing mechanism. Furthermore, the molecular docking of the receptor to both the microRNA and microRNA-mRNA duplex were analyzed computationally to understand their interaction at the molecular level. Conclusively, the study provides an explanation for understanding the microRNAs-based gene regulation (silencing mechanism) in CRC.

Site-Specific Cross-Linking of a p19 Viral Suppressor of RNA Silencing Protein and Its RNA Targets Using an Expanded Genetic Code

The p19 viral suppressor of RNA silencing protein has useful applications in biotechnology due to its high affinity for binding to small RNAs such as small interfering RNAs (siRNAs). Also, its applications for the study and modulation of microRNAs are actively expanding. Here we demonstrate the successful site-specific incorporation of a photoactivatable unnatural amino acid, p-azido-l-phenylalanine (AzF), for cross-linking to RNA substrates into the p19 sequence. Incorporation of AzF was performed at three positions in the protein near the RNA binding site: K67, R115, and T111. Incorporation of AzF at position T111 of p19 did not affect the binding affinity of p19 for siRNAs and also showed nanomolar affinity for human microRNA miR-122. The affinity was less favorable with AzF incorporation at two other positions, suggesting the sensitivity of placement of the unnatural amino acid. Exposure of the T111AzF in complex with either siRNA or miRNA to ultraviolet light resulted in cross-linking of the protein with the RNA, but no cross-linking could be detected with the wild-type protein. Our results demonstrate that p19-T111AzF can be used for detection of small RNAs, including human miR-122, with high sensitivity and to irreversibly sequester these RNAs through covalent photo-cross-linking.

mirDIP 4.1-integrative database of human microRNA target predictions

MicroRNAs are important regulators of gene expression, achieved by binding to the gene to be regulated. Even with modern high-throughput technologies, it is laborious and expensive to detect all possible microRNA targets. For this reason, several computational microRNA-target prediction tools have been developed, each with its own strengths and limitations. Integration of different tools has been a successful approach to minimize the shortcomings of individual databases. Here, we present mirDIP v4.1, providing nearly 152 million human microRNA-target predictions, which were collected across 30 different resources. We also introduce an integrative score, which was statistically inferred from the obtained predictions, and was assigned to each unique microRNA-target interaction to provide a unified measure of confidence. We demonstrate that integrating predictions across multiple resources does not cumulate prediction bias toward biological processes or pathways. mirDIP v4.1 is freely available at http://ophid.utoronto.ca/mirDIP/.

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

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

Effect of micro-RNA on tenocytes and tendon-related gene expression: A systematic review

The purpose of the review was to synthesize the current literature regarding the effect of miRNA on biological processes known to be involved in tendon and tenocyte development and homeostasis. Using multiple databases, a systematic review was performed with a customized search term crafted to identify any study examining micro-RNA in relation to tendon and/or tenocytes. Results were classified based on the following categories: Gene expression, tenocyte development and differentiation, tendon tissue repair, and tenocyte senescence. A total of 3,112 potentially relevant studies were reviewed, and after exclusion criteria was applied, 15 investigations were included in the final analysis. There were 14 specific miRNA included in this review, with 11 studies reporting on tendon-related gene expression, five reporting on tendon development and/or tenocyte differentiation, six reporting on tendon tissue repair, and five reporting on tenocyte senescence. The miR-29 family was the most commonly reported micro-RNA in the investigation. We also report on a number of micro-RNA which are associated with both positive and negative effects on tendon homeostasis. © 2018 Orthopaedic Research Society. Published by Wiley Periodicals, Inc. J Orthop Res 36:2823-2829, 2018.

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.

Association between polymorphisms in microRNA target sites and survival in early-stage non-small cell lung cancer

A high‐throughput mapping method of RNA–RNA interactions by crosslinking, ligation, and sequencing of hybrids (CLASH) can not only provide information about canonical but also non‐canonical interactions. We evaluated the associations between variants in microRNA target sites using CLASH data and survival outcomes of 782 early‐stage non‐small cell lung cancer (NSCLC) patients who underwent curative surgical resection. Among the 100 variants studied, two variants showed significant association with survival outcomes. The POLR2A rs2071504 C > T variant was associated with poor overall and disease‐free survival under a dominant model (hazard ratio [HR] 1.42, 95% confidence interval [CI] 1.08–1.88; P = 0.01 and HR 1.34, 95% CI 1.08–1.67; P = 0.01, respectively). Patients carrying the NR2F6 rs2288539 TT genotype showed significantly better overall survival than those with the NR2F6 rs2288539 CC or CT genotypes (HR 0.13, 95% CI 0.02–0.90; P = 0.04). These findings suggest that POLR2A rs2071504 C > T and NR2F6 rs2288539 C > T can influence prognosis in early‐stage NSCLC patients.

Global mapping of miRNA-target interactions in cattle (Bos taurus)

With roles in development, cell proliferation and disease, micro-RNA (miRNA) biology is of great importance and a potential therapeutic target. Here we used cross-linking immunoprecipitation (CLIP) and ligation of miRNA-target chimeras on the Argonaute (AGO) protein to globally map miRNA interactions in the cow. The interactome is the deepest reported to date. miRNA targeting principles are consistent with observations in other species, but with expanded pairing rules. Experimental mapping robustly predicted functional miR-17 regulatory sites. From miRNA-specific targeting for >5000 mRNAs we determined gene ontologies (GO). This confirmed repression of genes important for embryonic development and cell cycle progress by the let-7 family, and repression of those involved in cell cycle arrest by the miR-17 family, but also suggested a number of unappreciated miRNA functions. Our results provide a significant resource for understanding of bovine and species-conserved miRNA regulation, and demonstrate the power of experimental methods for establishing comprehensive interaction maps.