A computational approach for identifying microRNA-target interactions using high-throughput CLIP and PAR-CLIP sequencing

Non-coding RNAs in human health and disease: potential function as biomarkers and therapeutic targets

Human diseases have been a critical threat from the beginning of human history. Knowing the origin, course of action and treatment of any disease state is essential. A microscopic approach to the molecular field is a more coherent and accurate way to explore the mechanism, progression, and therapy with the introduction and evolution of technology than a macroscopic approach. Non-coding RNAs (ncRNAs) play increasingly important roles in detecting, developing, and treating all abnormalities related to physiology, pathology, genetics, epigenetics, cancer, and developmental diseases. Noncoding RNAs are becoming increasingly crucial as powerful, multipurpose regulators of all biological processes. Parallel to this, a rising amount of scientific information has revealed links between abnormal noncoding RNA expression and human disorders. Numerous non-coding transcripts with unknown functions have been found in addition to advancements in RNA-sequencing methods. Non-coding linear RNAs come in a variety of forms, including circular RNAs with a continuous closed loop (circRNA), long non-coding RNAs (lncRNA), and microRNAs (miRNA). This comprises specific information on their biogenesis, mode of action, physiological function, and significance concerning disease (such as cancer or cardiovascular diseases and others). This study review focuses on non-coding RNA as specific biomarkers and novel therapeutic targets.

Exosomal MicroRNA Discovery in Age-Related Macular Degeneration

Age-related macular degeneration (AMD) is a common condition causing progressive visual impairment, leading to irreversible blindness. Existing diagnostic tools for AMD are limited to clinical signs of drusen deposition in the macula and the visual assessment of the patient. The presence of circulating microRNAs (miRNAs) in the peripheral circulatory system with potential as diagnostic, prognostic, and/or predictive biomarkers has been reported in a number of conditions/diseases. miRNAs are key regulators of several biological processes, and miRNA dysregulation has been linked with numerous diseases, most remarkably cancer. miRNAs have been shown to be involved in AMD pathology, and several miRNA target genes and signalling pathways were associated with AMD pathogenesis. Exosomes are 50-90 nm membrane microvesicles (MVs), released by several cell types. Although exosomal functions are not completely understood, there is much evidence to suggest that exosomes play an essential role in cell-cell communication. They may stimulate target cells by transferring different bioactive molecules such as miRNA. Here we discuss methods to isolate exosome using serum specimens from AMD patients and miRNA profiling for the better understanding of the disease.

miRTarBase update 2022: an informative resource for experimentally validated miRNA-target interactions

MicroRNAs (miRNAs) are noncoding RNAs with 18–26 nucleotides; they pair with target mRNAs to regulate gene expression and produce significant changes in various physiological and pathological processes. In recent years, the interaction between miRNAs and their target genes has become one of the mainstream directions for drug development. As a large-scale biological database that mainly provides miRNA–target interactions (MTIs) verified by biological experiments, miRTarBase has undergone five revisions and enhancements. The database has accumulated >2 200 449 verified MTIs from 13 389 manually curated articles and CLIP-seq data. An optimized scoring system is adopted to enhance this update’s critical recognition of MTI-related articles and corresponding disease information. In addition, single-nucleotide polymorphisms and disease-related variants related to the binding efficiency of miRNA and target were characterized in miRNAs and gene 3′ untranslated regions. miRNA expression profiles across extracellular vesicles, blood and different tissues, including exosomal miRNAs and tissue-specific miRNAs, were integrated to explore miRNA functions and biomarkers. For the user interface, we have classified attributes, including RNA expression, specific interaction, protein expression and biological function, for various validation experiments related to the role of miRNA. We also used seed sequence information to evaluate the binding sites of miRNA. In summary, these enhancements render miRTarBase as one of the most research-amicable MTI databases that contain comprehensive and experimentally verified annotations. The newly updated version of miRTarBase is now available at https://miRTarBase.cuhk.edu.cn/.

PAR-CLIP: A Method for Transcriptome-Wide Identification of RNA Binding Protein Interaction Sites

During post-transcriptional gene regulation (PTGR), RNA binding proteins (RBPs) interact with all classes of RNA to control RNA maturation, stability, transport, and translation. Here, we describe Photoactivatable-Ribonucleoside-Enhanced Crosslinking and Immunoprecipitation (PAR-CLIP), a transcriptome-scale method for identifying RBP binding sites on target RNAs with nucleotide-level resolution. This method is readily applicable to any protein directly contacting RNA, including RBPs that are predicted to bind in a sequence- or structure-dependent manner at discrete RNA recognition elements (RREs), and those that are thought to bind transiently, such as RNA polymerases or helicases.

Methods to Investigate miRNA Function: Focus on Platelet Reactivity

MicroRNAs (miRNAs) are small noncoding RNAs modulating protein production. They are key players in regulation of cell function and are considered as biomarkers in several diseases. The identification of the proteins they regulate, and their impact on cell physiology, may delineate their role as diagnostic or prognostic markers and identify new therapeutic strategies. During the last 3 decades, development of a large panel of techniques has given rise to multiple models dedicated to the study of miRNAs. Since plasma samples are easily accessible, circulating miRNAs can be studied in clinical trials. To quantify miRNAs in numerous plasma samples, the choice of extraction and purification techniques, as well as normalization procedures, are important for comparisons of miRNA levels in populations and over time. Recent advances in bioinformatics provide tools to identify putative miRNAs targets that can then be validated with dedicated assays. In vitro and in vivo approaches aim to functionally validate candidate miRNAs from correlations and to understand their impact on cellular processes. This review describes the advantages and pitfalls of the available techniques for translational research to study miRNAs with a focus on their role in regulating platelet reactivity.

Techniques for Characterizing Cytomegalovirus-Encoded miRNAs

microRNAs (miRNAs) are small noncoding RNAs that regulate gene expression at the posttranscriptional level by binding to sites within the 3' untranslated regions of messenger RNA (mRNA) transcripts. The discovery of this completely new mechanism of gene regulation necessitated the development of a variety of techniques to further characterize miRNAs, their expression, and function. In this chapter, we will discuss techniques currently used in the miRNA field to detect, express and inhibit miRNAs, as well as methods used to identify and validate their targets, specifically with respect to the miRNAs encoded by human cytomegalovirus.

MicroRNAs Regulating Autophagy in Neurodegeneration

Social and economic impacts of neurodegenerative diseases (NDs) become more prominent in our constantly aging population. Currently, due to the lack of knowledge about the aetiology of most NDs, only symptomatic treatment is available for patients. Hence, researchers and clinicians are in need of solid studies on pathological mechanisms of NDs. Autophagy promotes degradation of pathogenic proteins in NDs, while microRNAs post-transcriptionally regulate multiple signalling networks including autophagy. This chapter will critically discuss current research advancements in the area of microRNAs regulating autophagy in NDs. Moreover, we will introduce basic strategies and techniques used in microRNA research. Delineation of the mechanisms contributing to NDs will result in development of better approaches for their early diagnosis and effective treatment.

LncRNA MEG3 Participates in Caerulein-Induced Inflammatory Injury in Human Pancreatic Cells via Regulating miR-195-5p/FGFR2 Axis and Inactivating NF-κB Pathway

Acute pancreatitis (AP) is a dysfunctional pancreas disease marked by severe inflammation. Long non-coding RNAs (lncRNAs) involving in the regulation of inflammatory responses have been frequently mentioned. The purpose of this study was to ensure the function and action mode of lncRNA maternally expressed gene 3 (MEG3) in caerulein-induced AP cell model. HPDE cells were treated with caerulein to establish an AP model in vitro. The expression of MEG3, miR-195-5p, and fibroblast growth factor receptor 2 (FGFR2) was measured using quantitative real-time polymerase chain reaction (qRT-PCR). Cell proliferation and apoptosis were detected by 3-(4, 5-dimethyl-2-thiazolyl)-2,5-diphenyl-2-H-tetrazolium bromide (MTT) assay and flow cytometry assay, respectively. The expression of CyclinD1, B cell lymphoma/leukemia-2 (Bcl-2), Bcl-2-associated X protein (Bax), FGFR2, P65, phosphorylated P65 (p-P65), alpha inhibitor of nuclear factor kappa beta (NF-κB) (IκB-α), and phosphorylated IκB-α (p-IκB-α) at the protein level was quantified by western blot. The concentrations of tumor necrosis factor α (TNF-α), interleukin-1β (IL-1β), and interleukin-6 (IL-6) were monitored by enzyme-linked immunosorbent assay (ELISA). The targeted relationship between miR-195-5p and MEG3 or FGFR2 was forecasted by the online software starBase v2.0 and verified by dual-luciferase reporter assay and RNA immunoprecipitation (RIP) assay. As a result, the expression of MEG3 and FGFR2 was decreased in caerulein-induced HPDE cells, while the expression of miR-195-5p was increased. MEG3 overexpression inhibited cell apoptosis and inflammatory responses that were induced by caerulein. Mechanically, miR-195-5p was targeted by MEG3 and abolished the effects of MEG3 overexpression. FGFR2 was a target of miR-195-5p, and MEG3 regulated the expression of FGFR2 by sponging miR-195-5p. FGFR2 overexpression abolished miR-195-5p enrichment-aggravated inflammatory injuries. Moreover, the NF-κB signaling pathway was involved in the MEG3/miR-195-5p/FGFR2 axis. Collectively, MEG3 participates in caerulein-induced inflammatory injuries by targeting the miR-195-5p/FGFR2 regulatory axis via mediating the NF-κB pathway in HPDE cells.

Dysregulated lncRNA-miRNA-mRNA Network Reveals Patient Survival-Associated Modules and RNA Binding Proteins in Invasive Breast Carcinoma

Breast cancer is the most common cancer in women, but few biomarkers are effective in clinic. Previous studies have shown the important roles of non-coding RNAs in diagnosis, prognosis, and therapy selection for breast cancer and have suggested the significance of integrating molecules at different levels to interpret the mechanism of breast cancer. Here, we collected transcriptome data including long non-coding RNA (lncRNA), microRNA (miRNA), and mRNA for ~1,200 samples, including 1079 invasive breast carcinoma samples and 104 normal samples, from The Cancer Genome Atlas (TCGA) project. We identified differentially expressed lncRNAs, miRNAs, and mRNAs that distinguished invasive carcinoma samples from normal samples. We further constructed an integrated dysregulated network consisting of differentially expressed lncRNAs, miRNAs, and mRNAs and found housekeeping and cancer-related functions. Moreover, 58 RNA binding proteins (RBPs) involved in biological processes that are essential to maintain cell survival were found in the dysregulated network, and 10 were correlated with overall survival. In addition, we identified two modules that stratify patients into high- and low-risk subgroups. The expression patterns of these two modules were significantly different in invasive carcinoma versus normal samples, and some molecules were high-confidence biomarkers of breast cancer. Together, these data demonstrated an important clinical application for improving outcome prediction for invasive breast cancers.

Trends in the development of miRNA bioinformatics tools

MicroRNAs (miRNAs) are small noncoding RNAs that regulate gene expression via recognition of cognate sequences and interference of transcriptional, translational or epigenetic processes. Bioinformatics tools developed for miRNA study include those for miRNA prediction and discovery, structure, analysis and target prediction. We manually curated 95 review papers and ∼1000 miRNA bioinformatics tools published since 2003. We classified and ranked them based on citation number or PageRank score, and then performed network analysis and text mining (TM) to study the miRNA tools development trends. Five key trends were observed: (1) miRNA identification and target prediction have been hot spots in the past decade; (2) manual curation and TM are the main methods for collecting miRNA knowledge from literature; (3) most early tools are well maintained and widely used; (4) classic machine learning methods retain their utility; however, novel ones have begun to emerge; (5) disease-associated miRNA tools are emerging. Our analysis yields significant insight into the past development and future directions of miRNA tools.

Interpreting and integrating big data in non-coding RNA research

In the last two decades, we have witnessed an impressive crescendo of non-coding RNA studies, due to both the development of high-throughput RNA-sequencing strategies and an ever-increasing awareness of the involvement of newly discovered ncRNA classes in complex regulatory networks. Together with excitement for the possibility to explore previously unknown layers of gene regulation, these advancements led to the realization of the need for shared criteria of data collection and analysis and for novel integrative perspectives and tools aimed at making biological sense of very large bodies of molecular information. In the last few years, efforts to respond to this need have been devoted mainly to the regulatory interactions involving ncRNAs as direct or indirect regulators of protein-coding mRNAs. Such efforts resulted in the development of new computational tools, allowing the exploitation of the information spread in numerous different ncRNA data sets to interpret transcriptome changes under physiological and pathological cell responses. While experimental validation remains essential to identify key RNA regulatory interactions, the integration of ncRNA big data, in combination with systematic literature mining, is proving to be invaluable in identifying potential new players, biomarkers and therapeutic targets in cancer and other diseases.

miRTarBase update 2018: a resource for experimentally validated microRNA-target interactions

MicroRNAs (miRNAs) are small non-coding RNAs of ∼ 22 nucleotides that are involved in negative regulation of mRNA at the post-transcriptional level. Previously, we developed miRTarBase which provides information about experimentally validated miRNA-target interactions (MTIs). Here, we describe an updated database containing 422 517 curated MTIs from 4076 miRNAs and 23 054 target genes collected from over 8500 articles. The number of MTIs curated by strong evidence has increased ∼1.4-fold since the last update in 2016. In this updated version, target sites validated by reporter assay that are available in the literature can be downloaded. The target site sequence can extract new features for analysis via a machine learning approach which can help to evaluate the performance of miRNA-target prediction tools. Furthermore, different ways of browsing enhance user browsing specific MTIs. With these improvements, miRTarBase serves as more comprehensively annotated, experimentally validated miRNA-target interactions databases in the field of miRNA related research. miRTarBase is available at http://miRTarBase.mbc.nctu.edu.tw/.

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.

PAR-CLIP for Discovering Target Sites of RNA-Binding Proteins

RNA-binding proteins (RBPs) establish posttranscriptional gene regulation (PTGR) by coordinating the maturation, editing, transport, stability, and translation of cellular RNAs. A variety of experimental approaches have been developed to characterize the RNAs associated with RBPs in vitro as well as in vivo. Our laboratory developed Photoactivatable-Ribonucleoside-Enhanced Cross-Linking and Immunoprecipitation (PAR-CLIP), which in combination with next-generation sequencing enables the identification of RNA targets of RBPs at a nucleotide-level resolution. Here we present an updated and condensed step-by-step PAR-CLIP protocol followed by the description of our RNA-seq data analysis pipeline.

MicroRNAs in orthopaedic research: Disease associations, potential therapeutic applications, and perspectives

MicroRNAs (miRNAs) are small non-coding RNAs that function to control many cellular processes by their ability to suppress expression of specific target genes. Tens to hundreds of target genes may be affected by one miRNA, thereby resulting in modulation of multiple pathways in any given cell type. Therefore, altered expression of miRNAs (i.e., during tissue development or in scenarios of disease or cellular stress) can have a profound impact on processes regulating cell differentiation, metabolism, proliferation, or apoptosis, for example. Over the past 5-10 years, thousands of reports have been published on miRNAs in cartilage and bone biology or disease, thus highlighting the significance of these non-coding RNAs in regulating skeletal development and homeostasis. For the purpose of this review, we will focus on miRNAs or miRNA families that have demonstrated function in vivo within the context of cartilage, bone or other orthopaedic-related tissues (excluding muscle). Specifically, we will discuss studies that have utilized miRNA transgenic mouse models or in vivo approaches to target a miRNA with the aim of altering conditions such as osteoarthritis, osteoporosis and bone fractures in rodents. We will not discuss miRNAs in the context skeletal cancers since this topic is worthy of a review of its own. Overall, we aim to provide a comprehensive description of where the field currently stands with respect to the therapeutic potential of specific miRNAs to treat orthopaedic conditions and current technologies to target and modify miRNA function in vivo. © 2017 Orthopaedic Research Society. Published by Wiley Periodicals, Inc. J Orthop Res 36:33-51, 2018.

CRISPR Genome Engineering for Human Pluripotent Stem Cell Research

The emergence of targeted and efficient genome editing technologies, such as repurposed bacterial programmable nucleases (e.g., CRISPR-Cas systems), has abetted the development of cell engineering approaches. Lessons learned from the development of RNA-interference (RNA-i) therapies can spur the translation of genome editing, such as those enabling the translation of human pluripotent stem cell engineering. In this review, we discuss the opportunities and the challenges of repurposing bacterial nucleases for genome editing, while appreciating their roles, primarily at the epigenomic granularity. First, we discuss the evolution of high-precision, genome editing technologies, highlighting CRISPR-Cas9. They exist in the form of programmable nucleases, engineered with sequence-specific localizing domains, and with the ability to revolutionize human stem cell technologies through precision targeting with greater on-target activities. Next, we highlight the major challenges that need to be met prior to bench-to-bedside translation, often learning from the path-to-clinic of complementary technologies, such as RNA-i. Finally, we suggest potential bioinformatics developments and CRISPR delivery vehicles that can be deployed to circumvent some of the challenges confronting genome editing technologies en route to the clinic.

Optimization of PAR-CLIP for transcriptome-wide identification of binding sites of RNA-binding proteins

Photoactivatable-Ribonucleoside-Enhanced Crosslinking and Immunoprecipitation (PAR-CLIP) in combination with next-generation sequencing is a powerful method for identifying endogenous targets of RNA-binding proteins (RBPs). Depending on the characteristics of each RBP, key steps in the PAR-CLIP procedure must be optimized. Here we present a comprehensive step-by-step PAR-CLIP protocol with detailed explanations of the critical steps. Furthermore, we report the application of a new PAR-CLIP data analysis pipeline to three distinct RBPs targeting different annotation categories of cellular RNAs.

Computational and Experimental Identification of Tissue-Specific MicroRNA Targets

In this chapter we discuss computational methods for the prediction of microRNA (miRNA) targets. More specifically, we consider machine learning-based approaches and explain why these methods have been relatively unsuccessful in reducing the number of false positive predictions. Further we suggest approaches designed to improve their performance by considering tissue-specific target regulation. We argue that the miRNA targetome differs depending on the tissue type and introduce a novel algorithm that predicts miRNA targets specifically for colorectal cancer. We discuss features of miRNAs and target sites that affect target recognition, and how next-generation sequencing data can support the identification of novel miRNAs, differentially expressed miRNAs and their tissue-specific mRNA targets. In addition, we introduce some experimental approaches for the validation of miRNA targets as well as web-based resources sharing predicted and validated miRNA target interactions.

Exosomal MicroRNA Discovery in Age-Related Macular Degeneration

Age-related macular degeneration (AMD) is a common condition causing progressive visual impairment, leading to irreversible blindness. Existing diagnostic tools for AMD are limited to clinical signs in the macula and the visual assessment of the patient. The presence of circulating microRNAs (miRNAs) in the peripheral circulatory system with potential as diagnostic, prognostic and/or predictive biomarkers has been reported in a number of conditions/diseases. miRNAs are key regulators of several biological processes, and miRNA dysregulation has been linked with numerous diseases, most remarkably cancer. miRNAs have been shown to be involved in AMD pathology and several miRNAs target genes and signaling pathways were identified in relation to AMD pathogenesis. Exosomes are 50-90 nm membrane micro-vesicles (MVs), released by several cell types. Although exosomal functions are not completely understood, there is much evidence to suggest that exosomes play an essential role in cell-cell communication. They may stimulate target cells by transferring different bioactive molecules such as miRNA. Here we discuss methods to isolate exosome using serum specimens from AMD patients and miRNA profiling for the better understanding of the disease.

Bioinformatic tools for analysis of CLIP ribonucleoprotein data

Investigating the interactions of RNA-binding proteins (RBPs) with RNAs is a complex task for molecular and computational biologists. The molecular biology techniques and the computational approaches to understand RBP-RNA (or ribonucleoprotein, RNP) interactions have advanced considerably over the past few years and numerous and diverse software tools have been developed to analyze these data. Accordingly, laboratories interested in RNP biology face the challenge of choosing adequately among the available software tools those that best address the biological problem they are studying. Here, we focus on state-of-the-art molecular biology techniques that employ crosslinking and immunoprecipitation (CLIP) of an RBP to study and map RNP interactions. We review the different software tools and databases available to analyze the most widely used CLIP methods, HITS-CLIP, PAR-CLIP, and iCLIP. WIREs RNA 2017, 8:e1404. doi: 10.1002/wrna.1404 For further resources related to this article, please visit the WIREs website.

TarPmiR: a new approach for microRNA target site prediction

MOTIVATION

The identification of microRNA (miRNA) target sites is fundamentally important for studying gene regulation. There are dozens of computational methods available for miRNA target site prediction. Despite their existence, we still cannot reliably identify miRNA target sites, partially due to our limited understanding of the characteristics of miRNA target sites. The recently published CLASH (crosslinking ligation and sequencing of hybrids) data provide an unprecedented opportunity to study the characteristics of miRNA target sites and improve miRNA target site prediction methods.

RESULTS

Applying four different machine learning approaches to the CLASH data, we identified seven new features of miRNA target sites. Combining these new features with those commonly used by existing miRNA target prediction algorithms, we developed an approach called TarPmiR for miRNA target site prediction. Testing on two human and one mouse non-CLASH datasets, we showed that TarPmiR predicted more than 74.2% of true miRNA target sites in each dataset. Compared with three existing approaches, we demonstrated that TarPmiR is superior to these existing approaches in terms of better recall and better precision.

AVAILABILITY AND IMPLEMENTATION

The TarPmiR software is freely available at http://hulab.ucf.edu/research/projects/miRNA/TarPmiR/ CONTACTS: haihu@cs.ucf.edu or xiaoman@mail.ucf.edu

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Tiny giants of gene regulation: experimental strategies for microRNA functional studies

The discovery over two decades ago of short regulatory microRNAs (miRNAs) has led to the inception of a vast biomedical research field dedicated to understanding these powerful orchestrators of gene expression. Here we aim to provide a comprehensive overview of the methods and techniques underpinning the experimental pipeline employed for exploratory miRNA studies in animals. Some of the greatest challenges in this field have been uncovering the identity of miRNA-target interactions and deciphering their significance with regard to particular physiological or pathological processes. These endeavors relied almost exclusively on the development of powerful research tools encompassing novel bioinformatics pipelines, high-throughput target identification platforms, and functional target validation methodologies. Thus, in an unparalleled manner, the biomedical technology revolution unceasingly enhanced and refined our ability to dissect miRNA regulatory networks and understand their roles in vivo in the context of cells and organisms. Recurring motifs of target recognition have led to the creation of a large number of multifactorial bioinformatics analysis platforms, which have proved instrumental in guiding experimental miRNA studies. Subsequently, the need for discovery of miRNA-target binding events in vivo drove the emergence of a slew of high-throughput multiplex strategies, which now provide a viable prospect for elucidating genome-wide miRNA-target binding maps in a variety of cell types and tissues. Finally, deciphering the functional relevance of miRNA post-transcriptional gene silencing under physiological conditions, prompted the evolution of a host of technologies enabling systemic manipulation of miRNA homeostasis as well as high-precision interference with their direct, endogenous targets. For further resources related to this article, please visit the WIREs website.

MicroRNAs: exploring new horizons in osteoarthritis

INTRODUCTION

Osteoarthritis (OA) is a common disease worldwide leading to significant morbidity. The underlying disease process is multifactorial however there is increasing focus on molecular mechanisms. MicroRNAs are small non-coding segments of RNA that have important regulatory functions at a cellular level. These molecules are readily detectable in human tissues and circulation. They are increasingly recognised as having a major role in many disease processes - including malignancy and inflammatory processes.

OBJECTIVE

This review paper aims to provide a comprehensive update on the evidence for miRNA roles in OA.

DESIGN

A comprehensive literature search was performed using key medical subject headings (MeSH) terms 'microRNA' and 'osteoarthritis'.

RESULTS

Several miRNAs have been identified as having aberrant expression levels in OA. Some of these include miR-9, miR-27, miR-34a, miR-140, miR-146a, miR-558 and miR-602. Many of the dysregulated miRNAs have been shown to regulate expression of inflammatory pathways such as interleukin-mediated or matrix metalloproteinase-13 (MMP-13)-mediated degradation of the articular cartilage extracellular matrix (ECM). MiRNAs may also play a role in pain pathways and hence expression of clinical symptoms.

CONCLUSIONS

Recent evidence has shown that miRNAs in the circulation may reflect underlying disease states and hence serve as potential markers for disease activity. These findings may represent possible future therapeutic applications in the management of OA.

PAR-CLIP: A Method for Transcriptome-Wide Identification of RNA Binding Protein Interaction Sites

During post-transcriptional gene regulation (PTGR), RNA binding proteins (RBPs) interact with all classes of RNA to control RNA maturation, stability, transport, and translation. Here, we describe Photoactivatable-Ribonucleoside-Enhanced Crosslinking and Immunoprecipitation (PAR-CLIP), a transcriptome-scale method for identifying RBP binding sites on target RNAs with nucleotide-level resolution. This method is readily applicable to any protein directly contacting RNA, including RBPs that are predicted to bind in a sequence- or structure-dependent manner at discrete RNA recognition elements (RREs), and those that are thought to bind transiently, such as RNA polymerases or helicases.

Mapping the Transcriptome-Wide Landscape of RBP Binding Sites Using gPAR-CLIP-seq: Bioinformatic Analysis

Protein-RNA interactions are integral components of posttranscriptional gene regulatory processes including mRNA processing and assembly of cellular architectures. Dysregulation of RNA-binding protein (RBP) expression or disruptions in RBP-RNA interactions underlie a variety of human pathologies and genetic diseases including cancer and neurodegenerative diseases (reviewed in (Cooper et al., Cell 136(4):777-793, 2009; Darnell, Cancer Res Treat 42(3):125-129, 2010; Lukong et al., Trends Genet 24 (8):416-425, 2008)). Recent studies have uncovered only a small proportion of the extensive RBP-RNA interactome in any organism (Baltz et al., Mol Cell 46(5):674-690, 2012; Castello et al., Cell 149(6):1393-1406, 2012; Freeberg et al., Genome Biol 14(2):R13, 2013; Hogan et al., PLoS Biol 6(10):e255, 2008; Mitchell et al., Nat Struct Mol Biol 20(1):127-133, 2013; Tsvetanova et al. PLoS One 5(9): pii: e12671, 2010; Schueler et al., Genome Biol 15(1):R15, 2014; Silverman et al., Genome Biol 15(1):R3, 2014). To expand our understanding of how RBP-RNA interactions govern RNA-related processes, we developed gPAR-CLIP-seq (global photoactivatable-ribonucleoside-enhanced cross-linking and precipitation followed by deep sequencing) for capturing and sequencing all regions of the Saccharomyces cerevisiae transcriptome bound by RBPs (Freeberg et al., Genome Biol 14(2):R13, 2013). This chapter describes a pipeline for bioinformatic analysis of gPAR-CLIP-seq data. The first half of this pipeline can be implemented by running locally installed programs or by running the programs using the Galaxy platform (Blankenberg et al., Curr Protoc Mol Biol. Chapter 19:Unit 19 10 11-21, 2010; Giardine et al., Genome Res 15 (10):1451-1455, 2005; Goecks et al., Genome Biol 11(8):R86, 2010). The second half of this pipeline can be implemented by user-generated code in any language using the pseudocode provided as a template.

MicroRNA target prediction using thermodynamic and sequence curves

BACKGROUND

MicroRNAs (miRNAs) are small regulatory RNA that mediate RNA interference by binding to various mRNA target regions. There have been several computational methods for the identification of target mRNAs for miRNAs. However, these have considered all contributory features as scalar representations, primarily, as thermodynamic or sequence-based features. Further, a majority of these methods solely target canonical sites, which are sites with "seed" complementarity. Here, we present a machine-learning classification scheme, titled Avishkar, which captures the spatial profile of miRNA-mRNA interactions via smooth B-spline curves, separately for various input features, such as thermodynamic and sequence features. Further, we use a principled approach to uniformly model canonical and non-canonical seed matches, using a novel seed enrichment metric.

RESULTS

We demonstrate that large number of seed-match patterns have high enrichment values, conserved across species, and that majority of miRNA binding sites involve non-canonical matches, corroborating recent findings. Using spatial curves and popular categorical features, such as target site length and location, we train a linear SVM model, utilizing experimental CLIP-seq data. Our model significantly outperforms all established methods, for both canonical and non-canonical sites. We achieve this while using a much larger candidate miRNA-mRNA interaction set than prior work.

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, specifically about 20% better than the state-of-the-art, for different species (human or mouse), or different target types (canonical or non-canonical). To the best of our knowledge we provide the first distributed framework for microRNA target prediction based on Apache Hadoop and Spark.

AVAILABILITY

All source code and data is publicly available at https://bitbucket.org/cellsandmachines/avishkar.

miRTarBase 2016: updates to the experimentally validated miRNA-target interactions database

MicroRNAs (miRNAs) are small non-coding RNAs of approximately 22 nucleotides, which negatively regulate the gene expression at the post-transcriptional level. This study describes an update of the miRTarBase (http://miRTarBase.mbc.nctu.edu.tw/) that provides information about experimentally validated miRNA-target interactions (MTIs). The latest update of the miRTarBase expanded it to identify systematically Argonaute-miRNA-RNA interactions from 138 crosslinking and immunoprecipitation sequencing (CLIP-seq) data sets that were generated by 21 independent studies. The database contains 4966 articles, 7439 strongly validated MTIs (using reporter assays or western blots) and 348 007 MTIs from CLIP-seq. The number of MTIs in the miRTarBase has increased around 7-fold since the 2014 miRTarBase update. The miRNA and gene expression profiles from The Cancer Genome Atlas (TCGA) are integrated to provide an effective overview of this exponential growth in the miRNA experimental data. These improvements make the miRTarBase one of the more comprehensively annotated, experimentally validated miRNA-target interactions databases and motivate additional miRNA research efforts.

Revealing protein-lncRNA interaction

Long non-coding RNAs (lncRNAs) are associated to a plethora of cellular functions, most of which require the interaction with one or more RNA-binding proteins (RBPs); similarly, RBPs are often able to bind a large number of different RNAs. The currently available knowledge is already drawing an intricate network of interactions, whose deregulation is frequently associated to pathological states. Several different techniques were developed in the past years to obtain protein–RNA binding data in a high-throughput fashion. In parallel, in silico inference methods were developed for the accurate computational prediction of the interaction of RBP–lncRNA pairs. The field is growing rapidly, and it is foreseeable that in the near future, the protein–lncRNA interaction network will rise, offering essential clues for a better understanding of lncRNA cellular mechanisms and their disease-associated perturbations.

Design and bioinformatics analysis of genome-wide CLIP experiments

The past decades have witnessed a surge of discoveries revealing RNA regulation as a central player in cellular processes. RNAs are regulated by RNA-binding proteins (RBPs) at all post-transcriptional stages, including splicing, transportation, stabilization and translation. Defects in the functions of these RBPs underlie a broad spectrum of human pathologies. Systematic identification of RBP functional targets is among the key biomedical research questions and provides a new direction for drug discovery. The advent of cross-linking immunoprecipitation coupled with high-throughput sequencing (genome-wide CLIP) technology has recently enabled the investigation of genome-wide RBP–RNA binding at single base-pair resolution. This technology has evolved through the development of three distinct versions: HITS-CLIP, PAR-CLIP and iCLIP. Meanwhile, numerous bioinformatics pipelines for handling the genome-wide CLIP data have also been developed. In this review, we discuss the genome-wide CLIP technology and focus on bioinformatics analysis. Specifically, we compare the strengths and weaknesses, as well as the scopes, of various bioinformatics tools. To assist readers in choosing optimal procedures for their analysis, we also review experimental design and procedures that affect bioinformatics analyses.

Computational challenges, tools, and resources for analyzing co- and post-transcriptional events in high throughput

Co- and post-transcriptional regulation of gene expression is complex and multifaceted, spanning the complete RNA lifecycle from genesis to decay. High-throughput profiling of the constituent events and processes is achieved through a range of technologies that continue to expand and evolve. Fully leveraging the resulting data is nontrivial, and requires the use of computational methods and tools carefully crafted for specific data sources and often intended to probe particular biological processes. Drawing upon databases of information pre-compiled by other researchers can further elevate analyses. Within this review, we describe the major co- and post-transcriptional events in the RNA lifecycle that are amenable to high-throughput profiling. We place specific emphasis on the analysis of the resulting data, in particular the computational tools and resources available, as well as looking toward future challenges that remain to be addressed.

Prediction of miRNA targets

Computational methods for miRNA target prediction are currently undergoing extensive review and evaluation. There is still a great need for improvement of these tools and bioinformatics approaches are looking towards high-throughput experiments in order to validate predictions. The combination of large-scale techniques with computational tools will not only provide greater credence to computational predictions but also lead to the better understanding of specific biological questions. Current miRNA target prediction tools utilize probabilistic learning algorithms, machine learning methods and even empirical biologically defined rules in order to build models based on experimentally verified miRNA targets. Large-scale protein downregulation assays and next-generation sequencing (NGS) are now being used to validate methodologies and compare the performance of existing tools. Tools that exhibit greater correlation between computational predictions and protein downregulation or RNA downregulation are considered the state of the art. Moreover, efficiency in prediction of miRNA targets that are concurrently verified experimentally provides additional validity to computational predictions and further highlights the competitive advantage of specific tools and their efficacy in extracting biologically significant results. In this review paper, we discuss the computational methods for miRNA target prediction and provide a detailed comparison of methodologies and features utilized by each specific tool. Moreover, we provide an overview of current state-of-the-art high-throughput methods used in miRNA target prediction.

Computational Prediction of microRNA Targets

Computational prediction of microRNA (miRNA) targets is a fundamental step towards the characterization of miRNA function and the understanding of their role in disease. A single miRNA can regulate hundreds of different gene transcripts through partial sequence complementarity and a single gene may be regulated by several miRNAs acting cooperatively. The remarkable advances made in recent years have allowed the identification of key features for functional miRNA binding sites. A plethora of prediction tools are now available, but their accuracies remain rather poor, as miRNA target recognition has revealed itself to be a very complex and dynamic mechanism, still only partially understood.In this chapter, the principles of miRNA target prediction in animals are presented, together with the most up-to-date and effective computational approaches and tools available.

Argonaute CLIP-Seq reveals miRNA targetome diversity across tissue types

To date, analyses of individual targets have provided evidence of a miRNA targetome that extends beyond the boundaries of messenger RNAs (mRNAs) and can involve non-Watson-Crick base pairing in the miRNA seed region. Here we report our findings from analyzing 34 Argonaute HITS-CLIP datasets from several human and mouse cell types. Investigation of the architectural (i.e. bulge vs. contiguous pairs) and sequence (Watson-Crick vs. G:U pairs) preferences for human and mouse miRNAs revealed that many heteroduplexes are “non-canonical” i.e. their seed region comprises G:U and bulge combinations. The genomic distribution of miRNA targets differed distinctly across cell types but remained congruent across biological replicates of the same cell type. For some cell types intergenic and intronic targets were more frequent whereas in other cell types mRNA targets prevailed. The findings suggest an expanded model of miRNA targeting that is more frequent than the standard model currently in use. Lastly, our analyses of data from different cell types and laboratories revealed consistent Ago-loaded miRNA profiles across replicates whereas, unexpectedly, the Ago-loaded targets exhibited a much more dynamic behavior across biological replicates.

A model-based approach to identify binding sites in CLIP-Seq data

Cross-linking immunoprecipitation coupled with high-throughput sequencing (CLIP-Seq) has made it possible to identify the targeting sites of RNA-binding proteins in various cell culture systems and tissue types on a genome-wide scale. Here we present a novel model-based approach (MiClip) to identify high-confidence protein-RNA binding sites from CLIP-seq datasets. This approach assigns a probability score for each potential binding site to help prioritize subsequent validation experiments. The MiClip algorithm has been tested in both HITS-CLIP and PAR-CLIP datasets. In the HITS-CLIP dataset, the signal/noise ratios of miRNA seed motif enrichment produced by the MiClip approach are between 17% and 301% higher than those by the ad hoc method for the top 10 most enriched miRNAs. In the PAR-CLIP dataset, the MiClip approach can identify ∼50% more validated binding targets than the original ad hoc method and two recently published methods. To facilitate the application of the algorithm, we have released an R package, MiClip (http://cran.r-project.org/web/packages/MiClip/index.html), and a public web-based graphical user interface software (http://galaxy.qbrc.org/tool_runner?tool_id=mi_clip) for customized analysis.

PIPE-CLIP: a comprehensive online tool for CLIP-seq data analysis

CLIP-seq is widely used to study genome-wide interactions between RNA-binding proteins and RNAs. However, there are few tools available to analyze CLIP-seq data, thus creating a bottleneck to the implementation of this methodology. Here, we present PIPE-CLIP, a Galaxy framework-based comprehensive online pipeline for reliable analysis of data generated by three types of CLIP-seq protocol: HITS-CLIP, PAR-CLIP and iCLIP. PIPE-CLIP provides both data processing and statistical analysis to determine candidate cross-linking regions, which are comparable to those regions identified from the original studies or using existing computational tools. PIPE-CLIP is available at http://pipeclip.qbrc.org/.

dCLIP: a computational approach for comparative CLIP-seq analyses

Although comparison of RNA-protein interaction profiles across different conditions has become increasingly important to understanding the function of RNA-binding proteins (RBPs), few computational approaches have been developed for quantitative comparison of CLIP-seq datasets. Here, we present an easy-to-use command line tool, dCLIP, for quantitative CLIP-seq comparative analysis. The two-stage method implemented in dCLIP, including a modified MA normalization method and a hidden Markov model, is shown to be able to effectively identify differential binding regions of RBPs in four CLIP-seq datasets, generated by HITS-CLIP, iCLIP and PAR-CLIP protocols. dCLIP is freely available at http://qbrc.swmed.edu/software/.

SMMRNA: a database of small molecule modulators of RNA

We have developed SMMRNA, an interactive database, available at http://www.smmrna.org, with special focus on small molecule ligands targeting RNA. Currently, SMMRNA consists of ∼770 unique ligands along with structural images of RNA molecules. Each ligand in the SMMRNA contains information such as Kd, Ki, IC50, ΔTm, molecular weight (MW), hydrogen donor and acceptor count, XlogP, number of rotatable bonds, number of aromatic rings and 2D and 3D structures. These parameters can be explored using text search, advanced search, substructure and similarity-based analysis tools that are embedded in SMMRNA. A structure editor is provided for 3D visualization of ligands. Advance analysis can be performed using substructure and OpenBabel-based chemical similarity fingerprints. Upload facility for both RNA and ligands is also provided. The physicochemical properties of the ligands were further examined using OpenBabel descriptors, hierarchical clustering, binning partition and multidimensional scaling. We have also generated a 3D conformation database of ligands to support the structure and ligand-based screening. SMMRNA provides comprehensive resource for further design, development and refinement of small molecule modulators for selective targeting of RNA molecules.

The miRNA-mediated cross-talk between transcripts provides a novel layer of posttranscriptional regulation

Endogenously expressed transcripts that are posttranscriptionally regulated by the same microRNAs (miRNAs) will, in principle, compete for the binding of their shared small noncoding RNA regulators and modulate each other's abundance. Recently, the levels of some coding as well as noncoding transcripts have indeed been found to be regulated in this way. Transcripts that engage in such regulatory interactions are referred to as competitive endogenous RNAs (ceRNAs). This novel layer of posttranscriptional regulation has been shown to contribute to diverse aspects of organismal and cellular biology, despite the number of functionally characterized ceRNAs being as yet relatively low. Importantly, increasing evidence suggests that the dysregulation of some ceRNA interactions is associated with disease etiology, most preeminently with cancer. Here we review how posttranscriptional regulation by miRNAs contributes to the cross-talk between transcripts and review examples of known ceRNAs by highlighting the features underlying their interactions and what might be their biological relevance.

Computational methods for drug design and discovery: focus on China

In the past decades, China's computational drug design and discovery research has experienced fast development through various novel methodologies. Application of these methods spans a wide range, from drug target identification to hit discovery and lead optimization. In this review, we firstly provide an overview of China's status in this field and briefly analyze the possible reasons for this rapid advancement. The methodology development is then outlined. For each selected method, a short background precedes an assessment of the method with respect to the needs of drug discovery, and, in particular, work from China is highlighted. Furthermore, several successful applications of these methods are illustrated. Finally, we conclude with a discussion of current major challenges and future directions of the field.