Survey of Computational Algorithms for MicroRNA Target Prediction

In Silico Hybridization and Molecular Dynamics Simulations for the Identification of Candidate Human MicroRNAs for Inhibition of Virulent Proteins' Expression in Staphylococcus aureus

Staphylococcus aureus is a major threat to human health, causing infections that range in severity from moderate to fatal. The rising rates of antibiotic resistance highlight the critical need for new therapeutic techniques to combat this infection. It has been recently discovered that microRNAs (miRNAs) are essential for cross-kingdom communication, especially when it comes to host-pathogen interactions. It has been demonstrated that these short noncoding RNAs control gene expression in the gut microbiota, maintaining homeostasis; dysbiosis in this system has been linked to several diseases, including cancer. Our research attempts to use this understanding to target specific bacterial species and prevent severe diseases. In particular, we look for putative human miRNAs that can attach to virulent bacterial proteins' mRNA and prevent them from being expressed. In-silico hybridization experiments were performed between 100 human miRNA sequences with varied expression levels in gram-positive bacterial infections and five virulence factor genes. In addition, these miRNAs' binding properties were investigated using molecular dynamics (MD) simulations. Our findings demonstrate that human miRNAs can target and inhibit the expression of bacterial virulent genes, thereby opening up new paths for developing innovative miRNA-based therapeutics. The implementation of MD simulations in our study not only improves the validity of our findings but also proposes a new method for constructing miRNA-based therapies against life-threatening bacterial infections.

Artificial miRNAs and target-mimics as potential tools for crop improvement

MicroRNAs (miRNAs) are endogenous, small molecules that negatively regulate gene expression to control the normal development and stress response in plants. They mediate epigenetic changes and regulate gene expression at both transcriptional and post-transcriptional levels. Synthetic biology approaches have been utilized to design efficient artificial miRNAs (amiRNAs) or target-mimics to regulate specific gene expression for understanding the biological function of genes and crop improvement. The amiRNA based gene silencing is an effective technique to "turn off" gene expression, while miRNA target-mimics or decoys are used for efficiently down regulating miRNAs and "turn on" gene expression. In this context, the development of endogenous target-mimics (eTMs) and short tandem target mimics (STTMs) represent promising biotechnological tools for enhancing crop traits like stress tolerance and disease resistance. Through this review, we present the recent developments in understanding plant miRNA biogenesis, which is utilized for the efficient design and development of amiRNAs. This is important to incorporate the artificially synthesized miRNAs as internal components and utilizing miRNA biogenesis pathways for the programming of synthetic circuits to improve crop tolerance to various abiotic and biotic stress factors. The review also examines the recent developments in the use of miRNA target-mimics or decoys for efficiently down regulating miRNAs for trait improvement. A perspective analysis and challenges on the use of amiRNAs and STTM as potent tools to engineer useful traits in plants have also been presented.

PRIMITI: A computational approach for accurate prediction of miRNA-target mRNA interaction

Current medical research has been demonstrating the roles of miRNAs in a variety of cellular mechanisms, lending credence to the association between miRNA dysregulation and multiple diseases. Understanding the mechanisms of miRNA is critical for developing effective diagnostic and therapeutic strategies. miRNA-mRNA interactions emerge as the most important mechanism to be understood despite their experimental validation constraints. Accordingly, several computational models have been developed to predict miRNA-mRNA interactions, albeit presenting limited predictive capabilities, poor characterisation of miRNA-mRNA interactions, and low usability. To address these drawbacks, we developed PRIMITI, a PRedictive model for the Identification of novel miRNA-Target mRNA Interactions. PRIMITI is a novel machine learning model that utilises CLIP-seq and expression data to characterise functional target sites in 3'-untranslated regions (3'-UTRs) and predict miRNA-target mRNA repression activity. The model was trained using a reliable negative sample selection approach and the robust extreme gradient boosting (XGBoost) model, which was coupled with newly introduced features, including sequence and genetic variation information. PRIMITI achieved an area under the receiver operating characteristic (ROC) curve (AUC) up to 0.96 for a prediction of functional miRNA-target site binding and 0.96 for a prediction of miRNA-target mRNA repression activity on cross-validation and an independent blind test. Additionally, the model outperformed state-of-the-art methods in recovering miRNA-target repressions in an unseen microarray dataset and in a collection of validated miRNA-mRNA interactions, highlighting its utility for preliminary screening. PRIMITI is available on a reliable, scalable, and user-friendly web server at https://biosig.lab.uq.edu.au/primiti.

In Silico Prediction of NOS2, NOS3 and Arginase 1 Genes Targeting by Micro RNAs Upregulated in Systemic Sclerosis

Introduction:

Systemic sclerosis (SSc) is a chronic disorder, characterised by endothelial dysfunction and fibrosis of skin and internal organs. Endothelium dysfunction leads to a decrease in nitric oxide levels, consequent to reduced Nitric Oxide synthase 3 (NOS3) activity due to decreased NOS3 gene function. Besides endothelium, macrophages too play an important role in the pathogenesis of SSc; classically activated M1 macrophages (express NOS2) and alternatively activated M2 macrophages (express Arginase1) are differentially altered in favour of M2 macrophages. Micro-RNAs (miRNAs) regulate expression of various genes and may favour disease phenotype.

Aim:

Our aim was to identify differentially expressed micro-RNAs in SSc, which target NOS2, NOS3 and Arginase1 genes by in-silico approach.

Methods:

Data on miRNAs upregulation reported in various studies was collected and their sequence ID from miRBase was identified in FASTA format from NCBI. Binding strength of these miRNAs against NOS2, NOS3 and Arginase1 genes was evaluated by RNA22.

Results:

After scanning 39 publications reporting miRNA expression in SSc, a total of 13 miRNAs were found to be up-regulated for NOS2 (Gibbs free energy ≤18.0Kj/mol) and 15 miRNAs up-regulated for NOS3 (Gibbs free energy ≤18.0Kj/mol) whereas for Arginase1 only 2 miRNAs were up-regulated.

Conclusion:

There is differential upregulation of miRNAs in SSc. Micro RNAs targeting NOS2 and NOS3 genes are highly up-regulated in comparison to Arginase 1. Role of epigenetic regulation of genes by miRNAs may play a key role in pathogenesis of SSc.

miRNA Biology in Chronic Lymphocytic Leukemia

microRNAs (miRNAs) are a class of small non-coding RNAs that play a crucial regulatory role in fundamental biological processes and have been implicated in various diseases, including cancer. The first evidence of the cancer-related function of miRNAs was discovered in chronic lymphocytic leukemia (CLL) in the early 2000s. Alterations in miRNA expression have since been shown to strongly influence the clinical course, prognosis, and response to treatment in patients with CLL. Therefore, the identification of specific miRNA alterations not only enhances our understanding of the molecular mechanisms underlying CLL but also holds promise for the development of novel diagnostic and therapeutic strategies. This review aims to provide a comprehensive summary of the current knowledge and recent insights into miRNA dysregulation in CLL, emphasizing its pivotal roles in disease progression, including the development of the lethal Richter syndrome, and to provide an update on the latest translational research in this field.

Milk exosomal microRNA profiling identified miR-375 and miR-199-5p for regulation of immune response during subclinical mastitis of crossbred cattle

BACKGROUND

The dairy industry has experienced significant economic losses as a result of mastitis, an inflammatory disease of cows, including both subclinical and clinical cases. Milk exosome microRNAs have gained attention due to their stable and selective wrapping nature, offering potential for the prognosis and diagnosis of bovine mastitis, the most common pathological condition of the mammary gland.

METHODS AND RESULTS

 In the present investigation, the microRNA profile of milk exosomes was explored using high-throughput small RNA sequencing data in sub-clinical mastitic and healthy crossbred Vrindavani cattle. In both groups, 349 microRNAs were identified, with 238 (68.19%) microRNAs co-expressed; however, 35 and 76 distinct microRNAs were found in subclinical mastitic and healthy cattle, respectively. Differential expression analysis revealed 11 microRNAs upregulated, and 18 microRNAs were downregulated in sub-clinical mastitic cattle. The functional annotation of the target genes of differentially expressed known and novel microRNAs including bta-miR-375, bta-miR-199-5p and bta-miR-12030 reveals their involvement in the regulation of immune response and inflammatory mechanisms and could be involved in development of mastitis.

CONCLUSIONS

The analysis of milk exosomal miRNAs cargos hold great promise as an approach to study the underlying molecular mechanisms associated with mastitis in high milk producing dairy cattle. Concurrently, the significantly downregulated miR-375 may upregulate key target genes, including CTLA4, IHH, IRF1, and IL7R. These genes are negative regulators of immune response pathways, which could be associated with impaired inflammatory mechanisms in mammary cells. According to the findings, bta-miR-375 could be a promising biomarker for the development of mastitis in dairy cattle.

Emerging role of miRNA in prostate cancer: A future era of diagnostic and therapeutics

Prostate cancer (PCa) is the most common cancer in men (20%) and is responsible for 6.8% (1/5) of all cancer-related deaths in men around the world. The development and spread of prostate cancer are driven by a wide variety of genomic changes and extensive epigenetic events. Because of this, the MicroRNA (miRNA) and associated molecular mechanisms involved in PCa genesis and aggressive were only partially identified until today. The miRNAs are a newly discovered category of regulatorsthat have recently been recognized to have a significant role in regulating numerous elements of cancer mechanisms, such as proliferation, differentiation, metabolism, and apoptosis. The miRNAs are a type of small (22-24 nucleotides), non-coding, endogenous, single-stranded RNA and work as potent gene regulators. Various types of cancer, including PCa, have found evidence that miRNA genes, which are often located in cancer-related genetic regions or fragile locations, have a role in the primary steps of tumorigenesis, either as oncogenes or tumorsuppressors. To explain the link between miRNAs and their function in the initiation and advancement of PCa, we conducted a preliminary assessment. The purpose of this research was to enhance our understanding of the connection between miRNA expression profiles and PCa by elucidating the fundamental processes of miRNA expression and the target genes.

The role of microRNAs in hepatocellular carcinoma: Therapeutic targeting of tumor suppressor and oncogenic genes

Hepatocellular carcinoma (HCC) is a severe malignant liver cancer with a poor prognosis and a high mortality rate. This carcinoma is a multistage process that begins with chronic hepatitis and progresses to cirrhosis, dysplastic nodules, and eventually HCC. However, the exact molecular etiology remains unclear. MicroRNAs (miRs) are small non-coding RNAs that modulate the expression of numerous genes. These molecules have become significant participants in several functions, including cell proliferation, differentiation, development, and tumorrelated properties. They have a pivotal role in carcinogenesis as oncogenes or tumor suppressor genes. Furthermore, some investigations have shown that particular miRs might be used as predictive or diagnostic markers and therapeutic targets in HCC therapy. This review study summarizes the current level of knowledge on the role of miRs in the initiation and progression of HCC.

Turning off a few overexpressed genes in prostate cancer with microRNAs using a 7mer-seed match model

PURPOSE

This research aims to identify the miRNAs that could target the genes overexpressed in prostate cancer so that miRNA-based therapeutics could be developed.

METHODS

A 7mer-m8 model of microRNA targeting was utilized in order to analyse the relationship between microRNAs and overexpressed genes. The efficiency of miRNA binding was investigated using various parameters namely free energy (AMFE), GC and GC3 content, translation efficiency, cosine similarity metric, mRNA stability, free energy of RNA duplex, and base compositional difference. BLAST2GO software was used to elucidate the functional roles of the genes overexpressed in prostate cancer.

RESULTS

The current research reveals that the coding sequences of the genes were found targeted with multiple miRNAs. For instance, the HPN gene was targeted by the microRNA miR-4279 at two distinct sites i.e. 263-278 and 746-761 in the coding sequence. In the present study, it was observed that the target region of the genes exhibited a comparatively high GC and GC3 contents in comparison to the flanking regions. A low translational rate and weak relationship between RSCU and tRNA were obtained which may be due to the absence of optimal codons.

CONCLUSION

In this study, we have uncovered the human miRNAs that have potential for binding to the coding sequences of 14 most overexpressed genes in prostate cancer and thereby could silence those genes.

Transcriptome-Wide Analysis of microRNA-mRNA Correlations in Tissue Identifies microRNA Targeting Determinants

MicroRNAs are small RNAs that regulate gene expression through complementary base pairing with their target mRNAs. A substantial understanding of microRNA target recognition and repression mechanisms has been reached using diverse empirical and bioinformatic approaches, primarily in vitro biochemical or cell culture perturbation settings. We sought to determine if rules of microRNA target efficacy could be inferred from extensive gene expression data of human tissues. A transcriptome-wide assessment of all the microRNA-mRNA canonical interactions' efficacy was performed using a normalized Spearman correlation (Z-score) between the abundance of the transcripts in the PRAD-TCGA dataset tissues (RNA-seq mRNAs and small RNA-seq for microRNAs, 546 samples). Using the Z-score of correlation as a surrogate marker of microRNA target efficacy, we confirmed hallmarks of microRNAs, such as repression of their targets, the hierarchy of preference for gene regions (3'UTR > CDS > 5'UTR), and seed length (6 mer < 7 mer < 8 mer), as well as the contribution of the 3'-supplementary pairing at nucleotides 13-16 of the microRNA. Interactions mediated by 6 mer + supplementary showed similar inferred repression as 7 mer sites, suggesting that the 6 mer + supplementary sites may be relevant in vivo. However, aggregated 7 mer-A1 seeds appear more repressive than 7 mer-m8 seeds, while similar when pairing possibilities at the 3'-supplementary sites. We then examined the 3'-supplementary pairing using 39 microRNAs with Z-score-inferred repressive 3'-supplementary interactions. The approach was sensitive to the offset of the bridge between seed and 3'-supplementary pairing sites, and the pattern of offset-associated repression found supports previous findings. The 39 microRNAs with effective repressive 3'supplementary sites show low GC content at positions 13-16. Our study suggests that the transcriptome-wide analysis of microRNA-mRNA correlations may uncover hints of microRNA targeting determinants. Finally, we provide a bioinformatic tool to identify microRNA-mRNA candidate interactions based on the sequence complementarity of the seed and 3'-supplementary regions.

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.

MicroRNA-Target Identification: A Combinatorial In Silico Approach

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

Degradome Assisted Plant MicroRNA Prediction Under Alternative Annotation Criteria

Current microRNA (miRNA) prediction methods are generally based on annotation criteria that tend to miss potential functional miRNAs. Recently, new miRNA annotation criteria have been proposed that could lead to improvements in miRNA prediction methods in plants. Here, we investigate the effect of the new criteria on miRNA prediction in Arabidopsis thaliana and present a new degradome assisted functional miRNA prediction approach. We investigated the effect by applying the new criteria, and a more permissive criteria on miRNA prediction using existing miRNA prediction tools. We also developed an approach to miRNA prediction that is assisted by the functional information extracted from the analysis of degradome sequencing. We demonstrate the improved performance of degradome assisted miRNA prediction compared to unassisted prediction and evaluate the approach using miRNA differential expression analysis. We observe how the miRNA predictions fit under the different criteria and show a potential novel miRNA that has been missed within Arabidopsis thaliana. Additionally, we introduce a freely available software 'PAREfirst' that employs the degradome assisted approach. The study shows that some miRNAs could be missed due to the stringency of the former annotation criteria, and combining a degradome assisted approach with more permissive miRNA criteria can expand confident miRNA predictions.

Inferring microRNA regulation: A proteome perspective

Post-transcriptional regulation in multicellular organisms is mediated by microRNAs. However, the principles that determine if a gene is regulated by miRNAs are poorly understood. Previous works focused mostly on miRNA seed matches and other features of the 3′-UTR of transcripts. These common approaches rely on knowledge of the miRNA families, and computational approaches still yield poor, inconsistent results, with many false positives. In this work, we present a different paradigm for predicting miRNA-regulated genes based on the encoded proteins. In a novel, automated machine learning framework, we use sequence as well as diverse functional annotations to train models on multiple organisms using experimentally validated data. We present insights from tens of millions of features extracted and ranked from different modalities. We show high predictive performance per organism and in generalization across species. We provide a list of novel predictions including Danio rerio (zebrafish) and Arabidopsis thaliana (mouse-ear cress). We compare genomic models and observe that our protein model outperforms, whereas a unified model improves on both. While most membranous and disease related proteins are regulated by miRNAs, the G-protein coupled receptor (GPCR) family is an exception, being mostly unregulated by miRNAs. We further show that the evolutionary conservation among paralogs does not imply any coherence in miRNA regulation. We conclude that duplicated paralogous genes that often changed their function, also diverse in their tendency to be miRNA regulated. We conclude that protein function is informative across species in predicting post-transcriptional miRNA regulation in living cells.

Structural, molecular hybridization and network based identification of miR-373-3p and miR-520e-3p as regulators of NR4A2 human gene involved in neurodegeneration

MicroRNAs (miRNAs) are short non-coding RNAs with a 22 nucleotide sequence length and docks to the 3'UTR/5'UTR of the gene to regulate their mRNA translation to play a vital role in neurodegenerative diseases. The Nuclear Receptor gene (NR4A2), a transcription factor, and a steroid-thyroid hormone retinoid receptor is involved in neural development, memory formation, dopaminergic neurotransmission, and cellular protection from inflammatory damage. Therefore, recognizing the miRNAs is essential to efficiently target the 3'UTR/5'UTR of the NR4A2 gene and regulate neurodegeneration. Highly stabilized top miRNA-mRNA hybridized structures, their homologs, and identification of the best structures based on their least free energy were evaluated using in silico techniques. The miR-gene, gene-gene network analysis, miR-disease association, and transcription factor binding sites were also investigated. Results suggest top 166 miRNAs targeting the NR4A2 mRNA, but with a total of 10 miRNAs bindings with 100% seed sequence identity (both at 3' and 5'UTR) at the same position on the NR4A2 mRNA region. The miR-373-3p and miR-520e-3p are considered the best candidate miRNAs hybridizing with high efficiency at both 3' and 5'UTR of NR4A2 mRNA. This could be due to the most significant seed sequence length complementary, supplementary pairing, and absence of non-canonical base pairs. Furthermore, the miR-gene network, target gene-gene interaction analysis, and miR-disease association provide an understanding of the molecular, cellular, and biological processes involved in various pathways regulated by four transcription factors (PPARG, ZNF740, NRF1, and RREB1). Therefore, miR-373-3p, 520e-3p, and four transcription factors can regulate the NR4A2 gene involved in the neurodegenerative process.

Binding sites of miRNA on the overexpressed genes of oral cancer using 7mer-seed match

The microRNAs having a length of ~ 19-22 nucleotides are the small, non-coding RNAs. The evolution of microRNAs in many disorders may hold the key to tackle complex challenges. Oral cancer belongs to the group of head and neck cancer. It occurs in the mouth region that appears as an ulcer. In this study, we collected information on the overexpressed genes of oral cancer. The coding sequences of the genes were derived from NCBI and the entire set of human microRNAs present in miRBASE 21 was retrieved. The human microRNAs that can target the overexpressed genes of oral cancer were determined with the aid of our in-house software. The interaction between microRNAs and the overexpressed genes was evaluated with 7mer-m8 model of microRNA targeting. The genes DKK1 and APLN paired with only one miRNA i.e., miR-447 and miR-6087, respectively. But the genes INHBA and MMP1 were found to be targeted by 2 miRNAs, while the genes FN1, FAP, TGFPI, COL4A1, COL4A2, and LOXL2 were found to be targeted by 16, 5, 9, 18, 29, and 11 miRNAs. Subsequently, several measures such as free energy, translation efficiency, and cosine similarity metric were used to estimate the binding process. It was found that the target region's stability was higher than the upstream and downstream zones. The overexpressed genes' GC contents were calculated, revealing that the codons in target miRNA region were overall GC rich as well as GC3 rich. Lastly, gene ontology was performed to better understand each gene's involvement in biological processes, molecular function, and cellular component. Our study showed the role of microRNAs in gene repression, which could possibly aid in the prognosis and diagnosis of oral cancer.

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.

Biological features between miRNAs and their targets are unveiled from deep learning models

MicroRNAs (miRNAs) are ~ 22 nucleotide ubiquitous gene regulators. They modulate a broad range of essential cellular processes linked to human health and diseases. Consequently, identifying miRNA targets and understanding how they function are critical for treating miRNA associated diseases. In our earlier work, a hybrid deep learning-based approach (miTAR) was developed for predicting miRNA targets. It performs substantially better than the existing methods. The approach integrates two major types of deep learning algorithms: convolutional neural networks (CNNs) and recurrent neural networks (RNNs). However, the features in miRNA:target interactions learned by miTAR have not been investigated. In the current study, we demonstrated that miTAR captures known features, including the involvement of seed region and the free energy, as well as multiple novel features, in the miRNA:target interactions. Interestingly, the CNN and RNN layers of the model perform differently at capturing the free energy feature: the units in RNN layer is more unique at capturing the feature but collectively the CNN layer is more efficient at capturing the feature. Although deep learning models are commonly thought “black-boxes”, our discoveries support that the biological features in miRNA:target can be unveiled from deep learning models, which will be beneficial to the understanding of the mechanisms in miRNA:target interactions.

Identification of novel microRNAs in Rous sarcoma Virus (RSV) and their target sites in tumor suppressor genes of chicken

A small non-coding, evolutionarily conserved regulatory RNA molecule known as microRNA (miRNA) regulates various cellular activities and pathways. MicroRNAs remain evolutionarily conserved in different species of same taxa. They are present in all organisms including viruses. Viral miRNAs are small, less conserved and less stable and have higher negative minimal folding free energy than miRNAs of different organisms. The size of viral precursor miRNA is approximately 60-119 nucleotides in length. The structure of the mature miRNA sequences is predicted by using higher negative MFE (ΔG) value. Rous sarcoma Virus (RSV), named after its inventor Peyton Rous, has been known for causing tumors in the chicken for which it is known as an oncogenic retrovirus. Using specific criteria we have predicted 5 potential miRNAs in RSV which targeted 8 tumor suppressor genes in Gallus gallus. This study aims to predict the potential miRNAs, secondary structures and their targets for better understanding of the regulatory network of Rous sarcoma virus miRNA in forming sarcoma.

Recent development of bioinformatics tools for microRNA target prediction

MicroRNAs (miRNAs) are central players that regulate the post-transcriptional processes of gene expression. Binding of miRNAs to target mRNAs can repress their translation by inducing the degradation or by inhibiting the translation of the target mRNAs. High-throughput experimental approaches for miRNA target identification are costly and time-consuming, depending on various factors. It is vitally important to develop the bioinformatics methods for accurately predicting miRNA targets. With the increase of RNA sequences in the post-genomic era, bioinformatics methods are being developed for miRNA studies specially for miRNA target prediction. This review summarizes the current development of state-of-the-art bioinformatics tools for miRNA target prediction, points out the progress and limitations of the available miRNA databases, and their working principles. Finally, we discuss the caveat and perspectives of the next-generation algorithms for the prediction of miRNA targets.

An updated overview and classification of bioinformatics tools for MicroRNA analysis, which one to choose?

The term 'MicroRNA' (miRNA) refers to a class of small endogenous non-coding RNAs (ncRNAs) regenerated from hairpin transcripts. Recent studies reveal miRNAs' regulatory involvement in essential biological processes through translational repression or mRNA degradation. Recently, there is a growing body of literature focusing on the importance of miRNAs and their functions. In this respect, several databases have been developed to manage the dispersed data produced. Therefore, it is necessary to know the parameters and characteristics of each database to benefit their data. Besides, selecting the correct database is of great importance to scientists who do not have enough experience in this field. A comprehensive classification along with an explanation of the information contained in each database leads to facilitating access to these resources. In this regard, we have classified relevant databases into several categories, including miRNA sequencing and annotation, validated/predicted miRNA targets, disease-related miRNA, SNP in miRNA sequence or target site, miRNA-related pathways, or gene ontology, and mRNA-miRNA interactions. Hence, this review introduces available miRNA databases and presents a convenient overview to inform researchers of different backgrounds to find suitable miRNA-related bioinformatics web tools and relevant information rapidly.

MicroRNAs and Oxidative Stress: An Intriguing Crosstalk to Be Exploited in the Management of Type 2 Diabetes

Type 2 diabetes is a chronic disease widespread throughout the world, with significant human, social, and economic costs. Its multifactorial etiology leads to persistent hyperglycemia, impaired carbohydrate and fat metabolism, chronic inflammation, and defects in insulin secretion or insulin action, or both. Emerging evidence reveals that oxidative stress has a critical role in the development of type 2 diabetes. Overproduction of reactive oxygen species can promote an imbalance between the production and neutralization of antioxidant defence systems, thus favoring lipid accumulation, cellular stress, and the activation of cytosolic signaling pathways, and inducing β-cell dysfunction, insulin resistance, and tissue inflammation. Over the last few years, microRNAs (miRNAs) have attracted growing attention as important mediators of diverse aspects of oxidative stress. These small endogenous non-coding RNAs of 19-24 nucleotides act as negative regulators of gene expression, including the modulation of redox signaling pathways. The present review aims to provide an overview of the current knowledge concerning the molecular crosstalk that takes place between oxidative stress and microRNAs in the physiopathology of type 2 diabetes, with a special emphasis on its potential as a therapeutic target.

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

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

Circulating miRNA in atherosclerosis: a clinical biomarker and early diagnostic tool

Atherosclerosis, which is a vascular disease, is characterized by narrowing the arteries and forming plaque inside arteries. There is a record 17.5 million associated deaths recorded annually, representing 31% of global death. It has been noted that there is an association between vascular fibrosis and atherosclerosis. The thickening of the arterial wall and reduction of the lumen diameter may cause unwarranted deposition of extracellular matrix (ECM), and these conditions help in the progression of many clinical diseases and pathological conditions such as atherosclerosis. Here, we reviewed the involvement of various circulating microRNAs (miRNAs) in the very early diagnosis of atherosclerosis. We have also tried to provide an insight into the advantages and validation of circulating miRNAs through different techniques. We have discussed different circulating miRNAs, such as miR-17, miR-17-5p, miR-29b, miR-30, miR-92a, miR-126, miR-143, miR-145, miR-146a, miR-212, miR-218, miR-221, miR-222, miR-361-5p, as a biomarker for clinical diagnosis of atherosclerosis. The insightful demonstration in this review will offer a better opportunity for the researchers and technology developers in understanding the current scenario of circulating miRNA, which could facilitate them in improving the current diagnostic technologies of atherosclerosis in clinics.

miR-128-3p inhibits apoptosis and inflammation in LPS-induced sepsis by targeting TGFBR2

Background

Sepsis is a systemic inflammatory response that can lead to the dysfunction of many organs. The aberrant expression of miRNAs is associated with the pathogenesis of sepsis. However, the biological functions of miR-128-3p in sepsis remain largely unknown, and its mechanism should be further investigated. This study aimed to determine the regulatory network of miR-128-3p and TGFBR2 in lipopolysaccharide (LPS)-induced sepsis.

Methods

The expression levels of miR-128-3p and transforming growth factor beta receptors II (TGFBR2) were detected by quantitative polymerase chain reaction (qPCR). The protein levels of TGFBR2, Bcl-2, Bax, cleaved caspase 3, Smad2, and Smad3 were measured by western blot. Cell apoptosis was analyzed by flow cytometry. Cytokine production was detected by enzyme-linked immunosorbent assay (ELISA). The binding sites of miR-128-3p and TGFBR2 were predicted by Targetscan online software and confirmed by dual-luciferase reporter assay and RNA immunoprecipitation (RIP) assay.

Results

The level of miR-128-3p was decreased, and TGFBR2 expression was increased in serum samples of sepsis patients and LPS-induced HK2 cells. Overexpression of miR-128-3p or knockdown of TGFBR2 ameliorated LPS-induced inflammation and apoptosis. Moreover, TGFBR2 was a direct target of miR-128-3p, and its overexpression reversed the inhibitory effects of miR-128-3p overexpression on inflammation and apoptosis in LPS-induced HK2 cells. Besides, overexpression of miR-128-3p downregulated TGFBR2 to suppress the activation of the Smad signaling pathway.

Conclusion

miR-128-3p could inhibit apoptosis and inflammation by targeting TGFBR2 in LPS-induced HK2 cells, which might provide therapeutic strategy for the treatment of sepsis.

Human MicroRNA Target Prediction via Multi-Hypotheses Learning

MicroRNAs are involved in many critical cellular activities through binding to their mRNA targets, for example, in cell proliferation, differentiation, death, growth control, and developmental timing. Prediction of microRNA targets can assist in efficient experimental investigations on the functional roles of these small noncoding RNAs. Their accurate prediction, however, remains a challenge due to the limited understanding of underlying processes in recognizing microRNA targets. In this article, we introduce an algorithm that aims at not only predicting microRNA targets accurately but also assisting in vivo experiments to understand the mechanisms of targeting. The algorithm learns a unique hypothesis for each possible mechanism of microRNA targeting. These hypotheses are utilized to build a superior target predictor and for biologically meaningful partitioning of the data set of microRNA-target duplexes. Experimentally verified features for recognizing targets that incorporated in the algorithm enable the establishment of hypotheses that can be correlated with target recognition mechanisms. Our results and analysis show that our algorithm outperforms state-of-the-art data-driven approaches such as deep learning models and machine learning algorithms and rule-based methods for instance miRanda and RNAhybrid. In addition, feature selection on the partitions, provided by our algorithm, confirms that the partitioning mechanism is closely related to biological mechanisms of microRNA targeting. The resulting data partitions can potentially be used for in vivo experiments to aid in the discovery of the targeting mechanisms.

Methylation Profile of miR-9-1 and miR-9-1/-9-3 as Potential Biomarkers of Diabetic Retinopathy

AIMS

Analysis of the relationship between the methylation profile of miR-9-1 or miRs -9-1 / -9-3 and diabetic retinopathy.

BACKGROUND

Diabetic Retinopathy (DR) is a frequent complication of Diabetes mellitus and it has a decisive impact on the quality of life, as it is one of the biggest causes of blindness in the adult population. Levels of microRNA-9 have been shown to be related to diabetes but little is known about its involvement with DR in humans.

OBJECTIVE

To analyze the relationship between the methylation profile of miR-9-1 or miRs -9-1/-9-3 and DR.

METHODS

103 patients diagnosed with diabetes for 5 to 10 years were analyzed. The data were categorized according to clinical, biochemical, lifestyle and anthropometric parameters. DNA extracted from leukocyte samples was used to determine the methylation profile of miRs-9-1 and -9-3 using a specific methylation PCR assay.

RESULTS

miR-9-1 methylation was related to diabetic retinopathy, indicating that methylation of this miR increases the chances of presenting retinopathy up to 5 times. In our analyses, diabetics with lower levels of creatinine and CRP showed significant reductions (99% and 97%) in presenting DR. Methylation of both miRs-9-1 and 9-3 methylated increases the chances of presenting DR by 8 times; in addition, a sedentary lifestyle can increase the risk for the same complication by up to 6 times.

CONCLUSION

Our results suggest that both methylation of miR-9-1 and e miRs-9-1 / 9-3 favors DR in patients with diabetes in a period of 5 to 10 years of diagnosis.

miRNA Targets: From Prediction Tools to Experimental Validation

MicroRNAs (miRNAs) are post-transcriptional regulators of gene expression in both animals and plants. By pairing to microRNA responsive elements (mREs) on target mRNAs, miRNAs play gene-regulatory roles, producing remarkable changes in several physiological and pathological processes. Thus, the identification of miRNA-mRNA target interactions is fundamental for discovering the regulatory network governed by miRNAs. The best way to achieve this goal is usually by computational prediction followed by experimental validation of these miRNA-mRNA interactions. This review summarizes the key strategies for miRNA target identification. Several tools for computational analysis exist, each with different approaches to predict miRNA targets, and their number is constantly increasing. The major algorithms available for this aim, including Machine Learning methods, are discussed, to provide practical tips for familiarizing with their assumptions and understanding how to interpret the results. Then, all the experimental procedures for verifying the authenticity of the identified miRNA-mRNA target pairs are described, including High-Throughput technologies, in order to find the best approach for miRNA validation. For each strategy, strengths and weaknesses are discussed, to enable users to evaluate and select the right approach for their interests.

MIPDH: A Novel Computational Model for Predicting microRNA-mRNA Interactions by DeepWalk on a Heterogeneous Network

Analysis of miRNA-target mRNA interaction (MTI) is of crucial significance in discovering new target candidates for miRNAs. However, the biological experiments for identifying MTIs have a high false positive rate and are high-priced, time-consuming, and arduous. It is an urgent task to develop effective computational approaches to enhance the investigation of miRNA-target mRNA relationships. In this study, a novel method called MIPDH is developed for miRNA-mRNA interaction prediction by using DeepWalk on a heterogeneous network. More specifically, MIPDH extracts two kinds of features, in which a biological behavior feature is learned using a network embedding algorithm on a constructed heterogeneous network derived from 17 kinds of associations among drug, disease, and 6 kinds of biomolecules, and the attribute feature is learned using the k-mer method on sequences of miRNAs and target mRNAs. Then, a random forest classifier is trained on the features combined with the biological behavior feature and attribute feature. When implementing a 5-fold cross-validation experiment, MIPDH achieved an average accuracy, sensitivity, specificity and AUC of 75.85, 74.37, 77.33%, and 0.8044, respectively. To further evaluate the performance of MIPDH, other classifiers and feature descriptors are conducted for comparisons. MIPDH can achieve a better performance. Additionally, case studies on hsa-miR-106b-5p, hsa-let-7d-5p, and hsa-let-7e-5p are also implemented. As a result, 14, 9, and 9 out of the top 15 targets that interacted with these miRNAs were verified using the experimental literature or other databases. All these prediction results indicate that MIPDH is an effective method for predicting miRNA-target mRNA interactions.

Transcriptome Characterization of Repressed Embryonic Myogenesis Due to Maternal Calorie Restriction

Fetal malnutrition decreases skeletal myofiber number and muscle mass in neonatal mammals, which increases the risk of developing obesity and diabetes in adult life. However, the associated molecular mechanisms remain unclear. Here, we investigated how the nutrient (calorie) availability affects embryonic myogenesis using a porcine model. Sows were given a normal or calorie restricted diet, following which skeletal muscle was harvested from the fetuses at 35, 55, and 90 days of gestation (dg) and used for histochemical analysis and high-throughput sequencing. We observed abrupt repression of primary myofiber formation following maternal calorie restriction (MCR). Transcriptome profiling of prenatal muscles revealed that critical genes and muscle-specific miRNAs associated with increased proliferation and myoblast differentiation were downregulated during MCR-induced repression of myogenesis. Moreover, we identified several novel miRNA-mRNA interactions through an integrative analysis of their expression profiles, devising a putative molecular network involved in the regulation of myogenesis. Interestingly, NC_010454.3_1179 was identified as a novel myogenic miRNA that can base-pair with sequences in the 3'-UTR of myogenic differentiation protein 1 (MyoD1). And we found that this UTR inhibited the expression of a linked reporter gene encoding a key myogenic regulatory factor, resulting in suppression of myogenesis. Our results greatly increase our understanding of the mechanisms underlying the nutrient-modulated myogenesis, and may also serve as a valuable resource for further investigation of fundamental developmental processes or assist in rational target selection ameliorating repressed myogenesis under fetal malnutrition.

Dissecting miRNA facilitated physiology and function in human breast cancer for therapeutic intervention

MicroRNAs (miRNAs) are key epigenomic regulators of biological processes in animals and plants. These small non coding RNAs form a complex networks that regulate cellular function and development. MiRNAs prevent translation by either inactivation or inducing degradation of mRNA, a major concern in post-transcriptional gene regulation. Aberrant regulation of gene expression by miRNAs is frequently observed in cancer. Overexpression of various 'oncomiRs' and silencing of tumor suppressor miRNAs are associated with various types of human cancers, although overall downregulation of miRNA expression is reported as a hallmark of cancer. Modulations of the total pool of cellular miRNA by alteration in genetic and epigenetic factors associated with the biogenesis of miRNA machinery. It also depends on the availability of cellular miRNAs from its store in the organelles which affect tumor development and cancer progression. Here, we have dissected the roles and pathways of various miRNAs during normal cellular and molecular functions as well as during breast cancer progression. Recent research works and prevailing views implicate that there are two major types of miRNAs; (i) intracellular miRNAs and (ii) extracellular miRNAs. Concept, that the functions of intracellular miRNAs are driven by cellular organelles in mammalian cells. Extracellular miRNAs function in cell-cell communication in extracellular spaces and distance cells through circulation. A detailed understanding of organelle driven miRNA function and the precise role of extracellular miRNAs, pre- and post-therapeutic implications of miRNAs in this scenario would open several avenues for further understanding of miRNA function and can be better exploited for the treatment of breast cancers.

Improving Bioinformatics Prediction of microRNA Targets by Ranks Aggregation

microRNAs are noncoding RNAs which downregulate a large number of target mRNAs and modulate cell activity. Despite continued progress, bioinformatics prediction of microRNA targets remains a challenge since available software still suffer from a lack of accuracy and sensitivity. Moreover, these tools show fairly inconsistent results from one another. Thus, in an attempt to circumvent these difficulties, we aggregated all human results of four important prediction algorithms (miRanda, PITA, SVmicrO, and TargetScan) showing additional characteristics in order to rerank them into a single list. Instead of deciding which prediction tool to use, our method clearly helps biologists getting the best microRNA target predictions from all aggregated databases. The resulting database is freely available through a webtool called miRabel¹ which can take either a list of miRNAs, genes, or signaling pathways as search inputs. Receiver operating characteristic curves and precision-recall curves analysis carried out using experimentally validated data and very large data sets show that miRabel significantly improves the prediction of miRNA targets compared to the four algorithms used separately. Moreover, using the same analytical methods, miRabel shows significantly better predictions than other popular algorithms such as MBSTAR, miRWalk, ExprTarget and miRMap. Interestingly, an F-score analysis revealed that miRabel also significantly improves the relevance of the top results. The aggregation of results from different databases is therefore a powerful and generalizable approach to many other species to improve miRNA target predictions. Thus, miRabel is an efficient tool to guide biologists in their search for miRNA targets and integrate them into a biological context.

Propofol suppresses proliferation and metastasis of colorectal cancer cells by regulating miR-124-3p.1/AKT3

BACKGROUND

Propofol, an extensively used intravenous anesthetic agents during cancer resection surgery, has been confirmed to execute anti-tumor effect on multiple cancers, including colorectal cancer (CRC). Although the role of propofol in CRC has been previously reported, its action mechanism remains poorly understood. This study further explored the biological function and underlying mechanism of propofol in CRC cells.

METHODS

The cell proliferation, migration and invasion were assessed by methylthiazolyldiphenyl-tetrazolium bromide (MTT) assay, wound healing assay and transwell assay, respectively. The expression levels microRNA-124-3p.1 (miR-124-3p.1) and AKT serine/threonine kinase 3 (AKT3) was analyzed by quantitative real-time polymerase chain reaction. Western blot assay was employed to measure the protein expression of MMP-9, Vimentin and Cyclin D1. The interaction between miR-124-3p.1 and AKT3 was predicted by TargetScan and confirmed by dual-luciferase reporter assay.

RESULTS

Propofol inhibited CRC cell proliferation, migration and invasion. Knockdown of miR-124-3p.1 or AKT3 upregulation reversed the inhibitory effects of propofol on CRC cell proliferation and metastasis. Besides, AKT3 was a direct target of miR-124-3p.1 and its overexpression abated the anti-tumor effect of miR-124-3p.1 on CRC cell proliferation and metastasis.

CONCLUSION

Propofol inhibited CRC cell proliferation, migration and invasion by upregulating miR-124-3p.1 and downregulating AKT3, providing a new sight for propofol treatment of CRC.

PAREsnip2: a tool for high-throughput prediction of small RNA targets from degradome sequencing data using configurable targeting rules

Small RNAs (sRNAs) are short, non-coding RNAs that play critical roles in many important biological pathways. They suppress the translation of messenger RNAs (mRNAs) by directing the RNA-induced silencing complex to their sequence-specific mRNA target(s). In plants, this typically results in mRNA cleavage and subsequent degradation of the mRNA. The resulting mRNA fragments, or degradome, provide evidence for these interactions, and thus degradome analysis has become an important tool for sRNA target prediction. Even so, with the continuing advances in sequencing technologies, not only are larger and more complex genomes being sequenced, but also degradome and associated datasets are growing both in number and read count. As a result, existing degradome analysis tools are unable to process the volume of data being produced without imposing huge resource and time requirements. Moreover, these tools use stringent, non-configurable targeting rules, which reduces their flexibility. Here, we present a new and user configurable software tool for degradome analysis, which employs a novel search algorithm and sequence encoding technique to reduce the search space during analysis. The tool significantly reduces the time and resources required to perform degradome analysis, in some cases providing more than two orders of magnitude speed-up over current methods.

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.

Beyond the seed: structural basis for supplementary microRNA targeting by human Argonaute2

microRNAs (miRNAs) guide Argonaute proteins to mRNAs targeted for repression. Target recognition occurs primarily through the miRNA seed region, composed of guide (g) nucleotides g2-g8. However, nucleotides beyond the seed are also important for some known miRNA-target interactions. Here, we report the structure of human Argonaute2 (Ago2) engaged with a target RNA recognized through both miRNA seed and supplementary (g13-g16) regions. Ago2 creates a "supplementary chamber" that accommodates up to five miRNA-target base pairs. Seed and supplementary chambers are adjacent to each other and can be bridged by an unstructured target loop of 1-15 nucleotides. Opening of the supplementary chamber may be constrained by tension in the miRNA 3' tail, as increases in miRNA length stabilize supplementary interactions. Contrary to previous reports, we demonstrate that optimal supplementary interactions can increase target affinity > 20-fold. These results provide a mechanism for extended miRNA targeting, suggest a function for 3' isomiRs in tuning miRNA targeting specificity, and indicate that supplementary interactions may contribute more to target recognition than is widely appreciated.

Comprehensive Overview and Assessment of microRNA Target Prediction Tools in Homo sapiens and Drosophila melanogaster

Background:

MicroRNAs (miRNAs) are small non-coding RNAs that control gene expression at the post-transcriptional level through complementary base pairing with the target mRNA, leading to mRNA degradation and blocking translation process. Many dysfunctions of these small regulatory molecules have been linked to the development and progression of several diseases. Therefore, it is necessary to reliably predict potential miRNA targets.

Objective:

A large number of computational prediction tools have been developed which provide a faster way to find putative miRNA targets, but at the same time, their results are often inconsistent. Hence, finding a reliable, functional miRNA target is still a challenging task. Also, each tool is equipped with different algorithms, and it is difficult for the biologists to know which tool is the best choice for their study.

Methods:

We analyzed eleven miRNA target predictors on Drosophila melanogaster and Homo sapiens by applying significant empirical methods to evaluate and assess their accuracy and performance using experimentally validated high confident mature miRNAs and their targets. In addition, this paper also describes miRNA target prediction algorithms, and discusses common features of frequently used target prediction tools.

Results:

The results show that MicroT, microRNA and CoMir are the best performing tool on Drosopihla melanogaster; while TargetScan and miRmap perform well for Homo sapiens. The predicted results of each tool were combined in order to improve the performance in both the datasets, but any significant improvement is not observed in terms of true positives.

Conclusion:

The currently available miRNA target prediction tools greatly suffer from a large number of false positives. Therefore, computational prediction of significant targets with high statistical confidence is still an open challenge.

The long non-coding RNA Cerox1 is a post transcriptional regulator of mitochondrial complex I catalytic activity

To generate energy efficiently, the cell is uniquely challenged to co-ordinate the abundance of electron transport chain protein subunits expressed from both nuclear and mitochondrial genomes. How an effective stoichiometry of this many constituent subunits is co-ordinated post-transcriptionally remains poorly understood. Here we show that Cerox1, an unusually abundant cytoplasmic long noncoding RNA (lncRNA), modulates the levels of mitochondrial complex I subunit transcripts in a manner that requires binding to microRNA-488-3p. Increased abundance of Cerox1 cooperatively elevates complex I subunit protein abundance and enzymatic activity, decreases reactive oxygen species production, and protects against the complex I inhibitor rotenone. Cerox1 function is conserved across placental mammals: human and mouse orthologues effectively modulate complex I enzymatic activity in mouse and human cells, respectively. Cerox1 is the first lncRNA demonstrated, to our knowledge, to regulate mitochondrial oxidative phosphorylation and, with miR-488-3p, represent novel targets for the modulation of complex I activity.

Significant improvement of miRNA target prediction accuracy in large datasets using meta-strategy based on comprehensive voting and artificial neural networks

BACKGROUND

Identifying mRNA targets of miRNAs is critical for studying gene expression regulation at the whole-genome level. Multiple computational tools have been developed to predict miRNA:mRNA interactions. Nonetheless, many of these tools are developed in various small datasets, which each represent a limited sample space. Thus, the prediction accuracy of these tools has not been systematically validated at a larger scale. Accordingly, comparing the prediction accuracy of these tools and determining their applicability become challenging. In addition, the accuracy of these tools, especially in large datasets, needs to be improved for broader applications.

RESULTS

In this project, a large dataset containing more than 46,600 miRNA:mRNA interactions was assembled and split into eleven subsets based on the availability of prediction scores of four individual predictors, which are miRanda, miRDB, PITA, and TargetScan. In each of these subsets, the predictive results of four individual predictors were integrated using decision-tree based artificial neural networks to make the meta-prediction. The decision-tree is used here to sort the predictive results of four individual predictors, and artificial neural networks are applied to make meta-prediction based on the outputs of individual predictors. In the decision tree, dual-threshold and two-step significance-voting were incorporated, information gain was analysed to select threshold values. The prediction performance of this new strategy was improved significantly in most of the eleven datasets comparing to the individual predictors and other meta-predictors, such as ComiR, under multi-fold cross-validation, as well as in independent datasets. The overall improvement of prediction accuracy in independent datasets is at least 9 percentile points comparing to the other predictors, and the percentage of improvement of F1 and MCC scores is at least 40% compared to the other predictors.

CONCLUSIONS

The combination of dual-threshold, two-step significance-voting, and analysis of information gain is very effective in optimizing the outcome of decision-tree, and further integration with artificial neural networks is critical for further improving the performance of meta-predictor. A new pipeline based on this integration for miRNA target prediction has been developed. A strategy using outputs of individual predictors to reorganize large-scale miRNA:mRNA interaction dataset has also been validated and used to evaluate the prediction accuracy of predictors. The predictor is available at: https://github.com/xueLab/mirTarDANN ).

Delineating the Dynamic Transcriptome Response of mRNA and microRNA during Zebrafish Heart Regeneration

Heart diseases are the leading cause of death for the vast majority of people around the world, which is often due to the limited capability of human cardiac regeneration. In contrast, zebrafish have the capacity to fully regenerate their hearts after cardiac injury. Understanding and activating these mechanisms would improve health in patients suffering from long-term consequences of ischemia. Therefore, we monitored the dynamic transcriptome response of both mRNA and microRNA in zebrafish at 1–160 days post cryoinjury (dpi). Using a control model of sham-operated and healthy fish, we extracted the regeneration specific response and further delineated the spatio-temporal organization of regeneration processes such as cell cycle and heart function. In addition, we identified novel (miR-148/152, miR-218b and miR-19) and previously known microRNAs among the top regulators of heart regeneration by using theoretically predicted target sites and correlation of expression profiles from both mRNA and microRNA. In a cross-species effort, we validated our findings in the dynamic process of rat myoblasts differentiating into cardiomyocytes-like cells (H9c2 cell line). Concluding, we elucidated different phases of transcriptomic responses during zebrafish heart regeneration. Furthermore, microRNAs showed to be important regulators in cardiomyocyte proliferation over time.

In silico identification and characterization of a diverse subset of conserved microRNAs in bioenergy crop Arundo donax L

MicroRNAs (miRNAs) are small non-coding RNA molecules involved in the post-transcriptional regulation of gene expression in plants. Arundo donax L. is a perennial C₃ grass considered one of the most promising bioenergy crops. Despite its relevance, many fundamental aspects of its biology still remain to be elucidated. In the present study we carried out the first in silico mining and tissue-specific characterization of microRNAs and their putative targets in A. donax. We identified a total of 141 miRNAs belonging to 14 families along with the corresponding primary miRNAs, precursor miRNAs and a total of 462 high-confidence predicted targets and novel target sites were validated by 5′-race. Gene Ontology functional annotation showed that miRNA targets are constituted mainly by transcription factors, but three of the newly validated targets are enzymes involved in novel functions like RNA editing, acyl lipid metabolism and post-Golgi trafficking. Folding variability of pre-miRNA loops and phylogenetic analyses indicate variable selective pressure acting on the different miRNA families. The set of miRNAs identified in this study will pave the road to further miRNA research in Arundo donax and contribute towards a better understanding of miRNA-mediated gene regulatory processes in other bioenergy crops.

Genome-scale MicroRNA target prediction through clustering with Dirichlet process mixture model

BACKGROUND

MicroRNA regulation is fundamentally responsible for fine-tuning the whole gene network in human and has been implicated in most physiological and pathological conditions. Studying regulatory impact of microRNA on various cellular and disease processes has resulted in numerous computational tools that investigate microRNA-mRNA interactions through the prediction of static binding site highly dependent on sequence pairing. However, what hindered the practical use of such target prediction is the interplay between competing and cooperative microRNA binding that complicates the whole regulatory process exceptionally.

RESULTS

We developed a new method for improved microRNA target prediction based on Dirichlet Process Gaussian Mixture Model (DPGMM) using a large collection of molecular features associated with microRNA, mRNA, and the interaction sites. Multiple validations based on microRNA-mRNA interactions reported in recent large-scale sequencing analyses and a screening test on the entire human transcriptome show that our model outperformed several state-of-the-art tools in terms of promising predictive power on binding sites specific to transcript isoforms with reduced false positive prediction. Last, we illustrated the use of predicted targets in constructing conditional microRNA-mediated gene regulation networks in human cancer.

CONCLUSION

The probability-based binding site prediction provides not only a useful tool for differentiating microRNA targets according to the estimated binding potential but also a capability highly important for exploring dynamic regulation where binding competition is involved.

The clinical significance of miR-335, miR-124, miR-218 and miR-484 downregulation in gastric cancer

Gastric cancer (GC) is one of the leading types of malignancy worldwide, particularly in Asian populations. Although the exact molecular mechanism of GC development remains unknown, microRNA (miRNA) has recently been shown to be involved. The current study aims to investigate the expression levels of bioinformatically ranked miRNAs in gastric tissues. Using bioinformatics tools, we prioritized miRNAs thought to be implicated in GC. Furthermore, polyA-qPCR was used to validate bioinformatics findings in 40 GC, 31 normal gastric tissue (NG) and 45 gastric dysplasia (GD) samples. As identified by bioinformatics analysis, miR-335 was shown to be the top-ranked miRNA implicated in GC. Moreover, a significant downregulation of miR-335, miR-124, miR-218 and miR-484 was found in GC and GD compared to NG samples. We found bioinformatics to be an efficient approach to finding candidate miRNAs relevant to GC development. Finally, the findings show that downregulation of miRNAs such as miR-124 and miR-218 in gastric tissue can be a significant indicator for neoplastic transformation.

Accurate prediction of human miRNA targets via graph modeling of the miRNA-target duplex

miRNAs are involved in many critical cellular activities through binding to their mRNA targets, e.g. in cell proliferation, differentiation, death, growth control, and developmental timing. Accurate prediction of miRNA targets can assist efficient experimental investigations on the functional roles of miRNAs. Their prediction, however, remains a challengeable task due to the lack of experimental data about the tertiary structure of miRNA-target binding duplexes. In particular, correlations of nucleotides in the binding duplexes may not be limited to the canonical Watson Crick base pairs (BPs) as they have been perceived; methods based on secondary structure prediction (typically minimum free energy (MFE)) have only had mix success. In this work, we characterized miRNA binding duplexes with a graph model to capture the correlations between pairs of nucleotides of an miRNA and its target sequences. We developed machine learning algorithms to train the graph model to predict the target sites of miRNAs. In particular, because imbalance between positive and negative samples can significantly deteriorate the performance of machine learning methods, we designed a novel method to re-sample available dataset to produce more informative data learning process. We evaluated our model and miRNA target prediction method on human miRNAs and target data obtained from mirTarBase, a database of experimentally verified miRNA-target interactions. The performance of our method in target prediction achieved a sensitivity of 86% with a false positive rate below 13%. In comparison with the state-of-the-art methods miRanda and RNAhybrid on the test data, our method outperforms both of them by a significant margin. The source codes, test sets and model files all are available at http://rna-informatics.uga.edu/?f=software&p=GraB-miTarget .

The search for microRNAs potentially involved in the selfrenewal maintaining of laboratory rat pluripotent stem cells

Self-renewal of cultured pluripotent stem cells is a complex process, which includes multiple functional and regulatory levels. Transcription factors, their target genes, chromatin modifiers, signaling pathways, and regulatory noncoding RNAs are involved in the maintaining of self-renewal. Studies of molecular and genetic bases of maintaining self-renewal and pluripotency in cultured mammalian cells are important to understand processes in preimplantation embryogenesis and to develop efficient techniques to obtain pluripotent stem cell lines for experimental biology and medicine. MicroRNAs (miRNAs) play an important role in pluripotency maintaining and reprogramming. However, involvement of this class of noncoding RNAs and functions of individual molecules are poorly studied. The goal of this study was the search for the miRNAs potentially involved in the pluripotency maintaining and reprogramming of Rattus norvegicus cells. We analyzed the expression of miRNAs in rat embryonic stem cells, induced pluripotent stem cells and embryonic fibroblasts using bioinformatic methods and data obtained with next generation sequencing. The analysis of differential expression between groups of rat pluripotent cells and fibroblasts, and the analysis of experimentally confirmed target genes of differentially expressed known rat miRNAs revealed novel potential players of pluripotency maintaining and reprogramming processes. In addition, novel members of these processes were revealed among novel rat miRNAs. The use of bioinformatic and systems biology approaches is the first step, which is necessary for choosing candidates for the subsequent experimental studies. The results obtained substantially improve our understanding of the self-renewal regulation system of the laboratory rat, a popular biomedical object, and our knowledge about the system in mammals.

Understanding Neurodevelopmental Disorders: The Promise of Regulatory Variation in the 3'UTRome

Neurodevelopmental disorders have a strong genetic component, but despite widespread efforts, the specific genetic factors underlying these disorders remain undefined for a large proportion of affected individuals. Given the accessibility of exome sequencing, this problem has thus far been addressed from a protein-centric standpoint; however, protein-coding regions only make up ∼1% to 2% of the human genome. With the advent of whole genome sequencing we are in the midst of a paradigm shift as it is now possible to interrogate the entire sequence of the human genome (coding and noncoding) to fill in the missing heritability of complex disorders. These new technologies bring new challenges, as the number of noncoding variants identified per individual can be overwhelming, making it prudent to focus on noncoding regions of known function, for which the effects of variation can be predicted and directly tested to assess pathogenicity. The 3'UTRome is a region of the noncoding genome that perfectly fulfills these criteria and is of high interest when searching for pathogenic variation related to complex neurodevelopmental disorders. Herein, we review the regulatory roles of the 3'UTRome as binding sites for microRNAs or RNA binding proteins, or during alternative polyadenylation. We detail existing evidence that these regions contribute to neurodevelopmental disorders and outline strategies for identification and validation of novel putatively pathogenic variation in these regions. This evidence suggests that studying the 3'UTRome will lead to the identification of new risk factors, new candidate disease genes, and a better understanding of the molecular mechanisms contributing to neurodevelopmental disorders.