Combinatorial ensemble miRNA target prediction of co-regulation networks with non-prediction data

miR-195b is required for proper cellular homeostasis in the elderly

Over the last decade we have witnessed an increasing number of studies revealing the functional role of non-coding RNAs in a multitude of biological processes, including cellular homeostasis, proliferation and differentiation. Impaired expression of non-coding RNAs can cause distinct pathological conditions, including herein those affecting the gastrointestinal and cardiorespiratory systems, respectively. miR-15/miR-16/miR-195 family members have been broadly implicated in multiple biological processes, including regulation of cell proliferation, apoptosis and metabolism within distinct tissues, such as heart, liver and lungs. While the functional contribution of miR-195a has been reported in multiple biological contexts, the role of miR-195b remains unexplored. In this study we dissected the functional role of miR-195b by generating CRISPR-Cas9 gene edited miR-195b deficient mice. Our results demonstrate that miR-195b is dispensable for embryonic development. miR-195b-/- mice are fertile and displayed no gross anatomical and/or morphological defects. Mechanistically, cell cycle regulation, metabolism and oxidative stress markers are distinctly impaired in the heart, liver and lungs of aged mice, a condition that is not overtly observed at midlife. The lack of overt functional disarray during embryonic development and early adulthood might be due to temporal and tissue-specific compensatory mechanisms driven by selective upregulation miR-15/miR-16/miR-195 family members. Overall, our data demonstrated that miR-195b is dispensable for embryonic development and adulthood but is required for cellular homeostasis in the elderly.

miRNA Expression Analysis of the Hippocampus in a Vervet Monkey Model of Fetal Alcohol Spectrum Disorder Reveals a Potential Role in Global mRNA Downregulation

MicroRNAs (miRNAs) are short-length non-protein-coding RNA sequences that post-transcriptionally regulate gene expression in a broad range of cellular processes including neuro- development and have previously been implicated in fetal alcohol spectrum disorders (FASD). In this study, we use our vervet monkey model of FASD to follow up on a prior multivariate (developmental age × ethanol exposure) mRNA analysis (GSE173516) to explore the possibility that the global mRNA downregulation we observed in that study could be related to miRNA expression and function. We report here a predominance of upregulated and differentially expressed miRNAs. Further, the 24 most upregulated miRNAs were significantly correlated with their predicted targets (Target Scan 7.2). We then explored the relationship between these 24 miRNAs and the fold changes observed in their paired mRNA targets using two prediction platforms (Target Scan 7.2 and miRwalk 3.0). Compared to a list of non-differentially expressed miRNAs from our dataset, the 24 upregulated and differentially expressed miRNAs had a greater impact on the fold changes of their corresponding mRNA targets across both platforms. Taken together, this evidence raises the possibility that ethanol-induced upregulation of specific miRNAs might contribute functionally to the general downregulation of mRNAs observed by multiple investigators in response to prenatal alcohol exposure.

Web Services for RNA-RNA Interaction Prediction

Non-coding RNAs have various biological functions such as translational regulation, and RNA-RNA interactions play essential roles in the mechanisms of action of these RNAs. Therefore, RNA-RNA interaction prediction is an important problem in bioinformatics, and many tools have been developed for the computational prediction of RNA-RNA interactions. In addition to the development of novel algorithms with high accuracy, the development and maintenance of web services is essential for enhancing usability by experimental biologists. In this review, we survey web services for RNA-RNA interaction predictions and introduce how to use primary web services. We present various prediction tools, including general interaction prediction tools, prediction tools for specific RNA classes, and RNA-RNA interaction-based RNA design tools. Additionally, we discuss the future perspectives of the development of RNA-RNA interaction prediction tools and the sustainability of web services.

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.

Precision machine learning to understand micro-RNA regulation in neurodegenerative diseases

Micro-RNAs (miRNAs) are short (∼21 nt) non-coding RNAs that regulate gene expression through the degradation or translational repression of mRNAs. Accumulating evidence points to a role of miRNA regulation in the pathogenesis of a wide range of neurodegenerative (ND) diseases such as, for example, Alzheimer’s disease, Parkinson’s disease, amyotrophic lateral sclerosis and Huntington disease (HD). Several systems level studies aimed to explore the role of miRNA regulation in NDs, but these studies remain challenging. Part of the problem may be related to the lack of sufficiently rich or homogeneous data, such as time series or cell-type-specific data obtained in model systems or human biosamples, to account for context dependency. Part of the problem may also be related to the methodological challenges associated with the accurate system-level modeling of miRNA and mRNA data. Here, we critically review the main families of machine learning methods used to analyze expression data, highlighting the added value of using shape-analysis concepts as a solution for precisely modeling highly dimensional miRNA and mRNA data such as the ones obtained in the study of the HD process, and elaborating on the potential of these concepts and methods for modeling complex omics data.

Computational Detection of MicroRNA Targets

MicroRNAs (miRNAs) are small noncoding RNAs that are recognized as posttranscriptional regulators of gene expression. These molecules have been shown to play important roles in several cellular processes. MiRNAs act on their target by guiding the RISC complex and binding to the mRNA molecule. Thus, it is recognized that the function of a miRNA is determined by the function of its target (s). By using high-throughput methodologies, novel miRNAs are being identified, but their functions remain uncharted. Target validation is crucial to properly understand the specific role of a miRNA in a cellular pathway. However, molecular techniques for experimental validation of miRNA-target interaction are expensive, time-consuming, laborious, and can be not accurate in inferring true interactions. Thus, accurate miRNA target predictions are helpful to understand the functions of miRNAs. There are several algorithms proposed for target prediction and databases containing miRNA-target information. However, these available computational tools for prediction still generate a large number of false positives and fail to detect a considerable number of true targets, which indicates the necessity of highly confident approaches to identify bona fide miRNA-target interactions. This chapter focuses on tools and strategies used for miRNA target prediction, by providing practical insights and outlooks.

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.

Metabolic and energetic benefits of microRNA-22 inhibition

INTRODUCTION

We previously demonstrated in primary cultures of human subcutaneous adipocytes and in a mouse model of diet-induced obesity that specific microRNA-22-3p antagomirs produce a significant reduction of fat mass and an improvement of several metabolic parameters. These effects are related to the activation of target genes such as KDM3A, KDM6B, PPARA, PPARGC1B and SIRT1 involved in lipid catabolism, thermogenesis, insulin sensitivity and glucose homeostasis.

RESEARCH DESIGN AND METHODS

We now report a dedicated study exploring over the course of 3 months the metabolic and energetic effects of subcutaneous administration of our first miR-22-3p antagomir drug candidate (APT-110) in adult C57BL/6 male mice. Body composition, various blood parameters and energy expenditure were measured at several timepoints between week 12 and week 27 of age.

RESULTS

Weekly subcutaneous injections of APT-110 for 12 weeks produced a sustained increase of energy expenditure as early as day 11 of treatment, a significant fat mass reduction, but no change of appetite nor physical activity. Insulin sensitivity as well as circulating glucose, cholesterol and leptin were improved. There was a dramatic reduction of liver steatosis after 3 months of active treatment. RNA sequencing revealed an activation of lipid metabolism pathways in a tissue-specific manner.

CONCLUSIONS

These original findings suggest that microRNA-22-3p inhibition could lead to a potent treatment of fat accumulation, insulin resistance, and related complex metabolic disorders such as obesity, type 2 diabetes mellitus and non-alcoholic fatty liver disease.

Prediction of miRNA targets by learning from interaction sequences

MicroRNAs (miRNAs) are involved in a diverse variety of biological processes through regulating the expression of target genes in the post-transcriptional level. So, it is of great importance to discover the targets of miRNAs in biological research. But, due to the short length of miRNAs and limited sequence complementarity to their gene targets in animals, it is challenging to develop algorithms to predict the targets of miRNA accurately. Here we developed a new miRNA target prediction algorithm using a multilayer convolutional neural network. Our model learned automatically the interaction patterns of the experiment-validated miRNA:target-site chimeras from the raw sequence, avoiding hand-craft selection of features by domain experts. The performance on test dataset is inspiring, indicating great generalization ability of our model. Moreover, considering the stability of miRNA:target-site duplexes, our method also showed good performance to predict the target transcripts of miRNAs.

Prediction of the miRNA interactome - Established methods and upcoming perspectives

MicroRNAs (miRNAs) are well-studied small noncoding RNAs involved in post-transcriptional gene regulation in a wide range of organisms, including mammals. Their function is mediated by base pairing with their target RNAs. Although many features required for miRNA-mediated repression have been described, the identification of functional interactions is still challenging. In the last two decades, numerous Machine Learning (ML) models have been developed to predict their putative targets. In this review, we summarize the biological knowledge and the experimental data used to develop these ML models. Recently, Deep Neural Network-based models have also emerged in miRNA interaction modeling. We thus outline established and emerging models to give a perspective on the future developments needed to improve the identification of genes directly regulated by miRNAs.

Combining feature selection and shape analysis uncovers precise rules for miRNA regulation in Huntington's disease mice

Background

MicroRNA (miRNA) regulation is associated with several diseases, including neurodegenerative diseases. Several approaches can be used for modeling miRNA regulation. However, their precision may be limited for analyzing multidimensional data. Here, we addressed this question by integrating shape analysis and feature selection into miRAMINT, a methodology that we used for analyzing multidimensional RNA-seq and proteomic data from a knock-in mouse model (Hdh mice) of Huntington’s disease (HD), a disease caused by CAG repeat expansion in huntingtin (htt). This dataset covers 6 CAG repeat alleles and 3 age points in the striatum and cortex of Hdh mice.

Results

Remarkably, compared to previous analyzes of this multidimensional dataset, the miRAMINT approach retained only 31 explanatory striatal miRNA-mRNA pairs that are precisely associated with the shape of CAG repeat dependence over time, among which 5 pairs with a strong change of target expression levels. Several of these pairs were previously associated with neuronal homeostasis or HD pathogenesis, or both. Such miRNA-mRNA pairs were not detected in cortex.

Conclusions

These data suggest that miRNA regulation has a limited global role in HD while providing accurately-selected miRNA-target pairs to study how the brain may compute molecular responses to HD over time. These data also provide a methodological framework for researchers to explore how shape analysis can enhance multidimensional data analytics in biology and disease.

miRPathDB 2.0: a novel release of the miRNA Pathway Dictionary Database

Since the initial release of miRPathDB, tremendous progress has been made in the field of microRNA (miRNA) research. New miRNA reference databases have emerged, a vast amount of new miRNA candidates has been discovered and the number of experimentally validated target genes has increased considerably. Hence, the demand for a major upgrade of miRPathDB, including extended analysis functionality and intuitive visualizations of query results has emerged. Here, we present the novel release 2.0 of the miRNA Pathway Dictionary Database (miRPathDB) that is freely accessible at https://mpd.bioinf.uni-sb.de/. miRPathDB 2.0 comes with a ten-fold increase of pre-processed data. In total, the updated database provides putative associations between 27 452 (candidate) miRNAs, 28 352 targets and 16 833 pathways for Homo sapiens, as well as interactions of 1978 miRNAs, 24 898 targets and 6511 functional categories for Mus musculus. Additionally, we analyzed publications citing miRPathDB to identify common use-cases and further extensions. Based on this evaluation, we added new functionality for interactive visualizations and down-stream analyses of bulk queries. In summary, the updated version of miRPathDB, with its new custom-tailored features, is one of the most comprehensive and advanced resources for miRNAs and their target pathways.

Long non-coding RNA LNC01133 promotes the tumorigenesis of ovarian cancer by sponging miR-126

BACKGROUND

Ovarian cancer (OC) is the gynecologic malignancy with the highest mortality rate (70%), and it is urgent to find out a powerful prognostic marker for OC patients. LncRNAs are recently thought to be oncogenes in various cancers, and its expression levels are validated that can be inhibited by miRNAs. There are several studies indicating that sponging miRNAs will contribute to the tumorigenesis of cancers.

METHODS

In the present study, bioinformatics analysis is used to explore the potential oncogene and its target miRNAs; QRT-PCR is performed to count the expression level of several genes; Flow cytometric analysis is conducted to assess the apoptosis rate of several cell lines; Western blot assays are used to evaluate the expression levels of several proteins; Cells proliferation, migration and invasion abilities are detected by CCK-8 assay, Wound scratch assay and Transwell invasion assay, respectively. In vivo experiments are performed to assess the influence of LNC01133 on the formation of tumor.

RESULTS

We found LNC01133 was related to poor survival of OC patients, and identified that LNC01133 had significant influence on OC cells' apoptosis, proliferation, migration and invasion abilities. Furthermore, we observed miR-126 could target LNC01133 and decreased the expression level of LNC01133 in OC cells. Therefore, we sponged miR-126 to further study the molecular mechanism of OC tumorigenesis, and found an elevation in proliferation, migration and invasion abilities of OC cells, which suggested that miR-126 could serve as a powerful prognostic marker for OC patients, and had great clinical significance on OC diagnosis and treatment.

CONCLUSION

We found LNC01133 was an oncogene in OC, which is targeted by miR-126. miR-126 served as a powerful prognostic marker for OC patients because of its ability of promoting OC tumorigenesis after sponging.

Freiburg RNA tools: a central online resource for RNA-focused research and teaching

The Freiburg RNA tools webserver is a well established online resource for RNA-focused research. It provides a unified user interface and comprehensive result visualization for efficient command line tools. The webserver includes RNA-RNA interaction prediction (IntaRNA, CopraRNA, metaMIR), sRNA homology search (GLASSgo), sequence-structure alignments (LocARNA, MARNA, CARNA, ExpaRNA), CRISPR repeat classification (CRISPRmap), sequence design (antaRNA, INFO-RNA, SECISDesign), structure aberration evaluation of point mutations (RaSE), and RNA/protein-family models visualization (CMV), and other methods. Open education resources offer interactive visualizations of RNA structure and RNA-RNA interaction prediction as well as basic and advanced sequence alignment algorithms. The services are freely available at http://rna.informatik.uni-freiburg.de.