Got target? Computational methods for microRNA target prediction and their extension

Advances in applications of artificial intelligence algorithms for cancer-related miRNA research

MiRNAs are a class of small non-coding RNAs, which regulate gene expression post-transcriptionally by partial complementary base pairing. Aberrant miRNA expressions have been reported in tumor tissues and peripheral blood of cancer patients. In recent years, artificial intelligence algorithms such as machine learning and deep learning have been widely used in bioinformatic research. Compared to traditional bioinformatic tools, miRNA target prediction tools based on artificial intelligence algorithms have higher accuracy, and can successfully predict subcellular localization and redistribution of miRNAs to deepen our understanding. Additionally, the construction of clinical models based on artificial intelligence algorithms could significantly improve the mining efficiency of miRNA used as biomarkers. In this article, we summarize recent development of bioinformatic miRNA tools based on artificial intelligence algorithms, focusing on the potential of machine learning and deep learning in cancer-related miRNA research.

Predictive Role of Cluster Bean (Cyamopsis tetragonoloba) Derived miRNAs in Human and Cattle Health

MicroRNAs (miRNAs) are small non-coding conserved molecules with lengths varying between 18-25nt. Plants miRNAs are very stable, and probably they might have been transferred across kingdoms via food intake. Such miRNAs are also called exogenous miRNAs, which regulate the gene expression in host organisms. The miRNAs present in the cluster bean, a drought tolerant legume crop having high commercial value, might have also played a regulatory role for the genes involved in nutrients synthesis or disease pathways in animals including humans due to dietary intake of plant parts of cluster beans. However, the predictive role of miRNAs of cluster beans for gene-disease association across kingdoms such as cattle and humans are not yet fully explored. Thus, the aim of the present study is to (i) find out the cluster bean miRNAs (cb-miRs) functionally similar to miRNAs of cattle and humans and predict their target genes' involvement in the occurrence of complex diseases, and (ii) identify the role of cb-miRs that are functionally non-similar to the miRNAs of cattle and humans and predict their targeted genes' association with complex diseases in host systems. Here, we predicted a total of 33 and 15 functionally similar cb-miRs (fs-cb-miRs) to human and cattle miRNAs, respectively. Further, Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis revealed the participation of targeted genes of fs-cb-miRs in 24 and 12 different pathways in humans and cattle, respectively. Few targeted genes in humans like LCP2, GABRA6, and MYH14 were predicted to be associated with disease pathways of Yesinia infection (hsa05135), neuroactive ligand-receptor interaction (hsa04080), and pathogenic Escherichia coli infection (hsa05130), respectively. However, targeted genes of fs-cb-miRs in humans like KLHL20, TNS1, and PAPD4 are associated with Alzheimer's, malignant tumor of the breast, and hepatitis C virus infection disease, respectively. Similarly, in cattle, targeted genes like ATG2B and DHRS11 of fs-cb-miRs participate in the pathways of Huntington disease and steroid biosynthesis, respectively. Additionally, the targeted genes like SURF4 and EDME2 of fs-cb-miRs are associated with mastitis and bovine osteoporosis, respectively. We also found a few cb-miRs that do not have functional similarity with human and cattle miRNAs but are found to target the genes in the host organisms and as well being associated with human and cattle diseases. Interestingly, a few genes such as NRM, PTPRE and SUZ12 were observed to be associated with Rheumatoid Arthritis, Asthma and Endometrial Stromal Sarcoma diseases, respectively, in humans and genes like SCNN1B associated with renal disease in cattle.

Vault RNAs (vtRNAs): Rediscovered non-coding RNAs with diverse physiological and pathological activities

The physicochemical characteristics of RNA admit non-coding RNAs to perform a different range of biological acts through various mechanisms and are involved in regulating a diversity of fundamental processes. Notably, some reports of pathological conditions have proved abnormal expression of many non-coding RNAs guides the ailment. Vault RNAs are a class of non-coding RNAs containing stem regions or loops with well-conserved sequence patterns that play a fundamental role in the function of vault particles through RNA-ligand, RNA-RNA, or RNA-protein interactions. Taken together, vault RNAs have been proposed to be involved in a variety of functions such as cell proliferation, nucleocytoplasmic transport, intracellular detoxification processes, multidrug resistance, apoptosis, and autophagy, and serve as microRNA precursors and signaling pathways. Despite decades of investigations devoted, the biological function of the vault particle or the vault RNAs is not yet completely cleared. In this review, the current scientific assertions of the vital vault RNAs functions were discussed.

Computational tools supporting known miRNA identification

The study of small RNAs is a field that is expanding quickly. Other functional short RNA molecules other than microRNAs, and gene expression regulators, have been found in animals and plants. MicroRNAs play a significant role in host-microbe interactions, and parasite microRNAs may affect the host's innate immunity. Furthermore, short RNAs are intriguing non-invasive biomarker possibilities because they can be found in physiological fluids. These trends suggest that for many researchers, quick and simple techniques for expression profiling and subsequent downstream analysis of miRNA-seq data are crucial. We selected sRNAtoolbox to make integrated sRNA research easier. Each tool can be used separately or to explore and analyze sRNAbench results in further depth. A special focus was placed on the tools' usability. We review available miRNA research tools to have an overview of the evaluation of the tools. Mainly we evaluate the tool sRNAtoolbox.

How to do RNA-RNA Interaction Prediction? A Use-Case Driven Handbook Using IntaRNA

Computational prediction of RNA-RNA interactions (RRI) is a central methodology for the specific investigation of inter-molecular RNA interactions and regulatory effects of non-coding RNAs like eukaryotic microRNAs or prokaryotic small RNAs. Available methods can be classified according to their underlying prediction strategies, each implicating specific capabilities and restrictions often not transparent to the non-expert user. Within this work, we review seven classes of RRI prediction strategies and discuss the advantages and limitations of respective tools, since such knowledge is essential for selecting the right tool in the first place.Among the RRI prediction strategies, accessibility-based approaches have been shown to provide the most reliable predictions. Here, we describe how IntaRNA, as one of the state-of-the-art accessibility-based tools, can be applied in various use cases for the task of computational RRI prediction. Detailed hands-on examples for individual RRI predictions as well as large-scale target prediction scenarios are provided. We illustrate the flexibility and capabilities of IntaRNA through the examples. Each example is designed using real-life data from the literature and is accompanied by instructions on interpreting the respective results from IntaRNA output. Our use-case driven instructions enable non-expert users to comprehensively understand and utilize IntaRNA's features for effective RRI predictions.

circRNA-sponging: a pipeline for extensive analysis of circRNA expression and their role in miRNA sponging

Motivation

Circular RNAs (circRNAs) are long noncoding RNAs (lncRNAs) often associated with diseases and considered potential biomarkers for diagnosis and treatment. Among other functions, circRNAs have been shown to act as microRNA (miRNA) sponges, preventing the role of miRNAs that repress their targets. However, there is no pipeline to systematically assess the sponging potential of circRNAs.

Results

We developed circRNA-sponging, a nextflow pipeline that (i) identifies circRNAs via backsplicing junctions detected in RNA-seq data, (ii) quantifies their expression values in relation to their linear counterparts spliced from the same gene, (iii) performs differential expression analysis, (iv) identifies and quantifies miRNA expression from miRNA-sequencing (miRNA-seq) data, (v) predicts miRNA binding sites on circRNAs, (vi) systematically investigates potential circRNA-miRNA sponging events, (vii) creates a network of competing endogenous RNAs and (viii) identifies potential circRNA biomarkers. We showed the functionality of the circRNA-sponging pipeline using RNA sequencing data from brain tissues, where we identified two distinct types of circRNAs characterized by a specific ratio of the number of the binding site to the length of the transcript. The circRNA-sponging pipeline is the first end-to-end pipeline to identify circRNAs and their sponging systematically with raw total RNA-seq and miRNA-seq files, allowing us to better indicate the functional impact of circRNAs as a routine aspect in transcriptomic research.

Availability and implementation

https://github.com/biomedbigdata/circRNA-sponging.

Supplementary information

Supplementary data are available at Bioinformatics Advances online.

Genetic and Epigenetic Host-Virus Network to Investigate Pathogenesis and Identify Biomarkers for Drug Repurposing of Human Respiratory Syncytial Virus via Real-World Two-Side RNA-Seq Data: Systems Biology and Deep-Learning Approach

Human respiratory syncytial virus (hRSV) affects more than 33 million people each year, but there are currently no effective drugs or vaccines approved. In this study, we first constructed a candidate host-pathogen interspecies genome-wide genetic and epigenetic network (HPI-GWGEN) via big-data mining. Then, we employed reversed dynamic methods via two-side host-pathogen RNA-seq time-profile data to prune false positives in candidate HPI-GWGEN to obtain the real HPI-GWGEN. With the aid of principal-network projection and the annotation of KEGG pathways, we can extract core signaling pathways during hRSV infection to investigate the pathogenic mechanism of hRSV infection and select the corresponding significant biomarkers as drug targets, i.e., TRAF6, STAT3, IRF3, TYK2, and MAVS. Finally, in order to discover potential molecular drugs, we trained a DNN-based DTI model by drug-target interaction databases to predict candidate molecular drugs for these drug targets. After screening these candidate molecular drugs by three drug design specifications simultaneously, i.e., regulation ability, sensitivity, and toxicity. We finally selected acitretin, RS-67333, and phenformin to combine as a potential multimolecule drug for the therapeutic treatment of hRSV infection.

Impaired angiogenesis in diabetic critical limb ischemia is mediated by a miR-130b/INHBA signaling axis

Patients with peripheral artery disease (PAD) and diabetes compose a high-risk population for development of critical limb ischemia (CLI) and amputation, although the underlying mechanisms remain poorly understood. Comparison of dysregulated microRNAs from diabetic patients with PAD and diabetic mice with limb ischemia revealed the conserved microRNA, miR-130b-3p. In vitro angiogenic assays demonstrated that miR-130b rapidly promoted proliferation, migration, and sprouting in endothelial cells (ECs), whereas miR-130b inhibition exerted antiangiogenic effects. Local delivery of miR-130b mimics into ischemic muscles of diabetic mice (db/db) following femoral artery ligation (FAL) promoted revascularization by increasing angiogenesis and markedly improved limb necrosis and amputation. RNA-Seq and gene set enrichment analysis from miR-130b-overexpressing ECs revealed the BMP/TGF-β signaling pathway as one of the top dysregulated pathways. Accordingly, overlapping downregulated transcripts from RNA-Seq and miRNA prediction algorithms identified that miR-130b directly targeted and repressed the TGF-β superfamily member inhibin-β-A (INHBA). miR-130b overexpression or siRNA-mediated knockdown of INHBA induced IL-8 expression, a potent angiogenic chemokine. Lastly, ectopic delivery of silencer RNAs (siRNA) targeting Inhba in db/db ischemic muscles following FAL improved revascularization and limb necrosis, recapitulating the phenotype of miR-130b delivery. Taken together, a miR-130b/INHBA signaling axis may provide therapeutic targets for patients with PAD and diabetes at risk of developing CLI.

In Silico Identification of Cassava Genome-Encoded MicroRNAs with Predicted Potential for Targeting the ICMV-Kerala Begomoviral Pathogen of Cassava

Cassava mosaic disease (CMD) is caused by several divergent species belonging to the genus Begomovirus (Geminiviridae) transmitted by the whitefly Bemisia tabaci cryptic species group. In India and other parts of Asia, the Indian cassava mosaic virus-Kerala (ICMV-Ker) is an emergent begomovirus of cassava causing damage that results in reduced yield loss and tuber quality. Double-stranded RNA-mediated interference (RNAi) is an evolutionary conserved mechanism in eukaryotes and highly effective, innate defense system to inhibit plant viral replication and/or translation. The objective of this study was to identify and characterize cassava genome-encoded microRNAs (mes-miRNA) that are predicted to target ICMV-Ker ssDNA-encoded mRNAs, based on four in silico algorithms: miRanda, RNA22, Tapirhybrid, and psRNA. The goal is to deploy the predicted miRNAs to trigger RNAi and develop cassava plants with resistance to ICMV-Ker. Experimentally validated mature cassava miRNA sequences (n = 175) were downloaded from the miRBase biological database and aligned with the ICMV-Ker genome. The miRNAs were evaluated for base-pairing with the cassava miRNA seed regions and to complementary binding sites within target viral mRNAs. Among the 175 locus-derived mes-miRNAs evaluated, one cassava miRNA homolog, mes-miR1446a, was identified to have a predicted miRNA target binding site, at position 2053 of the ICMV-Ker genome. To predict whether the cassava miRNA might bind predicted ICMV-Ker mRNA target(s) that could disrupt viral infection of cassava plants, a cassava locus-derived miRNA-mRNA regulatory network was constructed using Circos software. The in silico-predicted cassava locus-derived mes-miRNA-mRNA network corroborated interactions between cassava mature miRNAs and the ICMV-Ker genome that warrant in vivo analysis, which could lead to the development of ICMV-Ker resistant cassava plants.

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.

Systems Drug Design for Muscle Invasive Bladder Cancer and Advanced Bladder Cancer by Genome-Wide Microarray Data and Deep Learning Method with Drug Design Specifications

Bladder cancer is the 10th most common cancer worldwide. Due to the lack of understanding of the oncogenic mechanisms between muscle-invasive bladder cancer (MIBC) and advanced bladder cancer (ABC) and the limitations of current treatments, novel therapeutic approaches are urgently needed. In this study, we utilized the systems biology method via genome-wide microarray data to explore the oncogenic mechanisms of MIBC and ABC to identify their respective drug targets for systems drug discovery. First, we constructed the candidate genome-wide genetic and epigenetic networks (GWGEN) through big data mining. Second, we applied the system identification and system order detection method to delete false positives in candidate GWGENs to obtain the real GWGENs of MIBC and ABC from their genome-wide microarray data. Third, we extracted the core GWGENs from the real GWGENs by selecting the significant proteins, genes and epigenetics via the principal network projection (PNP) method. Finally, we obtained the core signaling pathways from the corresponding core GWGEN through the annotations of the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway to investigate the carcinogenic mechanisms of MIBC and ABC. Based on the carcinogenic mechanisms, we selected the significant drug targets NFKB1, LEF1 and MYC for MIBC, and LEF1, MYC, NOTCH1 and FOXO1 for ABC. To design molecular drug combinations for MIBC and ABC, we employed a deep neural network (DNN)-based drug-target interaction (DTI) model with drug specifications. The DNN-based DTI model was trained by drug-target interaction databases to predict the candidate drugs for MIBC and ABC, respectively. Subsequently, the drug design specifications based on regulation ability, sensitivity and toxicity were employed as filter criteria for screening the potential drug combinations of Embelin and Obatoclax for MIBC, and Obatoclax, Entinostat and Imiquimod for ABC from their candidate drugs. In conclusion, we not only investigated the oncogenic mechanisms of MIBC and ABC, but also provided promising therapeutic options for MIBC and ABC, respectively.

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.

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.

Identification of microRNAs in the Lyme Disease Vector Ixodes scapularis

MicroRNAs (miRNAs) are a class of small non-coding RNAs involved in many biological processes, including the immune pathways that control bacterial, parasitic, and viral infections. Pathogens probably modify host miRNAs to facilitate successful infection, so they might be useful targets for vaccination strategies. There are few data on differentially expressed miRNAs in the black-legged tick Ixodes scapularis after infection with Borrelia burgdorferi, the causative agent of Lyme disease in the United States. Small RNA sequencing and qRT-PCR analysis were used to identify and validate differentially expressed I. scapularis salivary miRNAs. Small RNA-seq yielded 133,465,828 (≥18 nucleotides) and 163,852,135 (≥18 nucleotides) small RNA reads from Borrelia-infected and uninfected salivary glands for downstream analysis using the miRDeep2 algorithm. As such, 254 miRNAs were identified across all datasets, 25 of which were high confidence and 51 low confidence known miRNAs. Further, 23 miRNAs were differentially expressed in uninfected and infected salivary glands: 11 were upregulated and 12 were downregulated upon pathogen infection. Gene ontology and network analysis of target genes of differentially expressed miRNAs predicted roles in metabolic, cellular, development, cellular component biogenesis, and biological regulation processes. Several Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, including sphingolipid metabolism; valine, leucine and isoleucine degradation; lipid transport and metabolism; exosome biogenesis and secretion; and phosphate-containing compound metabolic processes, were predicted as targets of differentially expressed miRNAs. A qRT-PCR assay was utilized to validate the differential expression of miRNAs. This study provides new insights into the miRNAs expressed in I. scapularis salivary glands and paves the way for their functional manipulation to prevent or treat B. burgdorferi infection.

An Insight Into the microRNA Profile of the Ectoparasitic Mite Varroa destructor (Acari: Varroidae), the Primary Vector of Honey Bee Deformed Wing Virus

The remarkably adaptive mite Varroa destructor is the most important honey bee ectoparasite. Varroa mites are competent vectors of deformed wing virus (DWV), and the Varroa-virus complex is a major determinant of annual honey bee colony mortality and collapse. MicroRNAs (miRNAs) are 22-24 nucleotide non-coding RNAs produced by all plants and animals and some viruses that influence biological processes through post-transcriptional regulation of gene expression. Knowledge of miRNAs and their function in mite biology remains limited. Here we constructed small RNA libraries from male and female V. destructor using Illumina's small RNA-Seq platform. A total of 101,913,208 and 91,904,732 small RNA reads (>18 nucleotides) from male and female mites were analyzed using the miRDeep2 algorithm. A conservative approach predicted 306 miRNAs, 18 of which were upregulated and 13 downregulated in female V. destructor compared with males. Quantitative real-time PCR validated the expression of selected differentially-expressed female Varroa miRNAs. This dataset provides a list of potential miRNA targets involved in regulating vital Varroa biological processes and paves the way for developing strategies to target Varroa and their viruses.

Identification of potential target genes in Homo sapiens, by miRNA of Triticum aestivum: A cross kingdom computational approach

Plant-derived miRNAs can be found in the human body after dietary intake, and they can affect post-transcriptional gene regulation in human. It is important to identify targets to determine the possible effects in human genes by using computational approach. In this study, 787 possible mRNAs human targets were predicted by 84 miRNAs of wheat. A total of 14 miRNAs were identified with individual binding to 33 mRNAs associated with schizophrenia, epilepsy, neurodevelopmental disorders, and various cancers, located in the 3′UTR of the mRNA. A functional enrichment was carried out, where the results showed associations to pathways such as dopaminergic synapse (hsa04728), and signaling pathways, significantly associated with the target genes. The prediction of target mRNAs in humans by wheat miRNAs, offer candidates that could facilitate the search and verification, which could be of relevance for future projects and therefor contribute in the therapeutic treatment of various human diseases.

Integrated transcriptome and small RNA sequencing in revealing miRNA-mediated regulatory network of floral bud break in Prunus mume

MicroRNAs is one class of small non-coding RNAs that play important roles in plant growth and development. Though miRNAs and their target genes have been widely studied in many plant species, their functional roles in floral bud break and dormancy release in woody perennials is still unclear. In this study, we applied transcriptome and small RNA sequencing together to systematically explore the transcriptional and post-transcriptional regulation of floral bud break in P. mume. Through expression profiling, we identified a few candidate genes and miRNAs during different developmental stage transitions. In total, we characterized 1,553 DEGs associated with endodormancy release and 2,084 DEGs associated with bud flush. Additionally, we identified 48 known miRNAs and 53 novel miRNAs targeting genes enriched in biological processes such as floral organ morphogenesis and hormone signaling transudation. We further validated the regulatory relationship between differentially expressed miRNAs and their target genes combining computational prediction, degradome sequencing, and expression pattern analysis. Finally, we integrated weighted gene co-expression analysis and constructed miRNA-mRNA regulatory networks mediating floral bud flushing competency. In general, our study revealed the miRNA-mediated networks in modulating floral bud break in P. mume. The findings will contribute to the comprehensive understanding of miRNA-mediated regulatory mechanism governing floral bud break and dormancy cycling in wood perennials.

The Role of Deregulated MicroRNAs in Age-Related Macular Degeneration Pathology

Purpose

We previously identified three microRNAs (miRNAs) with significantly increased expression in the serum of patients with age-related macular degeneration (AMD) compared with healthy controls. Our objective was to identify potential functional roles of these upregulated miRNAs (miR-19a, miR-126, and miR-410) in AMD, using computational tools for miRNAs prediction and identification, and to demonstrate the miRNAs target genes and signaling pathways. We also aim to demonstrate the pathologic role of isolated sera-derived exosomes from patients with AMD and controls using in vitro models.

Methods

miR-19a, miR-126, and miR-410 were investigated using bioinformatic approaches, including DIANA-mirPath and miR TarBase. Data on the resulting target genes and signaling pathways were incorporated with the differentially expressed miRNAs in AMD. Apoptosis markers, human apoptosis miRNAs polymerase chain reaction arrays and angiogenesis/vasculogenesis assays were performed by adding serum-isolated AMD patient or control patient derived exosomes into an in vitro human angiogenesis model and ARPE-19 cell lines.

Results

A number of pathways known to be involved in AMD development and progression were predicted, including the vascular endothelial growth factor signaling, apoptosis, and neurodegenerative pathways. The study also provides supporting evidence for the involvement of serum-isolated AMD-derived exosomes in the pathology of AMD, via apoptosis and/or angiogenesis.

Conclusions

miR-19a, miR-126, miR-410 and their target genes had a significant correlation with AMD pathogenesis. As such, they could be potential new targets as predictive biomarkers or therapies for patients with AMD.

Translational Relevance

The functional analysis and the pathologic role of altered miRNA expression in AMD may be applicable in developing new therapies for AMD through the disruption of individual or multiple pathophysiologic pathways.

Comprehensive machine-learning-based analysis of microRNA-target interactions reveals variable transferability of interaction rules across species

BACKGROUND

MicroRNAs (miRNAs) are small non-coding RNAs that regulate gene expression post-transcriptionally via base-pairing with complementary sequences on messenger RNAs (mRNAs). Due to the technical challenges involved in the application of high-throughput experimental methods, datasets of direct bona fide miRNA targets exist only for a few model organisms. Machine learning (ML)-based target prediction models were successfully trained and tested on some of these datasets. There is a need to further apply the trained models to organisms in which experimental training data are unavailable. However, it is largely unknown how the features of miRNA-target interactions evolve and whether some features have remained fixed during evolution, raising questions regarding the general, cross-species applicability of currently available ML methods.

RESULTS

We examined the evolution of miRNA-target interaction rules and used data science and ML approaches to investigate whether these rules are transferable between species. We analyzed eight datasets of direct miRNA-target interactions in four species (human, mouse, worm, cattle). Using ML classifiers, we achieved high accuracy for intra-dataset classification and found that the most influential features of all datasets overlap significantly. To explore the relationships between datasets, we measured the divergence of their miRNA seed sequences and evaluated the performance of cross-dataset classification. We found that both measures coincide with the evolutionary distance between the compared species.

CONCLUSIONS

The transferability of miRNA-targeting rules between species depends on several factors, the most associated factors being the composition of seed families and evolutionary distance. Furthermore, our feature-importance results suggest that some miRNA-target features have evolved while others remained fixed during the evolution of the species. Our findings lay the foundation for the future development of target prediction tools that could be applied to "non-model" organisms for which minimal experimental data are available.

AVAILABILITY AND IMPLEMENTATION

The code is freely available at https://github.com/gbenor/TPVOD .

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

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

Systems Approach to Pathogenic Mechanism of Type 2 Diabetes and Drug Discovery Design Based on Deep Learning and Drug Design Specifications

In this study, we proposed a systems biology approach to investigate the pathogenic mechanism for identifying significant biomarkers as drug targets and a systematic drug discovery strategy to design a potential multiple-molecule targeting drug for type 2 diabetes (T2D) treatment. We first integrated databases to construct the genome-wide genetic and epigenetic networks (GWGENs), which consist of protein–protein interaction networks (PPINs) and gene regulatory networks (GRNs) for T2D and non-T2D (health), respectively. Second, the relevant “real GWGENs” are identified by system identification and system order detection methods performed on the T2D and non-T2D RNA-seq data. To simplify network analysis, principal network projection (PNP) was thereby exploited to extract core GWGENs from real GWGENs. Then, with the help of KEGG pathway annotation, core signaling pathways were constructed to identify significant biomarkers. Furthermore, in order to discover potential drugs for the selected pathogenic biomarkers (i.e., drug targets) from the core signaling pathways, not only did we train a deep neural network (DNN)-based drug–target interaction (DTI) model to predict candidate drug’s binding with the identified biomarkers but also considered a set of design specifications, including drug regulation ability, toxicity, sensitivity, and side effects to sieve out promising drugs suitable for T2D.

Using exosomal miRNAs extracted from porcine follicular fluid to investigate their role in oocyte development

BACKGROUND

Follicular development is crucial to normal oocyte maturation, with follicular size closely related to oocyte maturation. To better understand the molecular mechanisms behind porcine oocyte maturation, we obtained exosomal miRNA from porcine follicular fluid (PFF). These miRNA samples were then sequenced and analyzed regarding their different follicular sizes, as described in the methods section.

RESULTS

First, these results showed that this process successfully isolated PFF exosomes. Nearly all valid reads from the PFF exosomal sequencing data were successfully mapped to the porcine genome database. Second, we used hierarchical clustering methods to determine that significantly expressed miRNAs were clustered into A, B, C, and D groups in our heatmap according to different follicle sizes. These results allowed for the targeting of potential mRNAs genes related to porcine oocyte development. Third, we chose ten, significantly expressed miRNAs and predicted their target genes for further GO analysis. These results showed that the expression levels of neurotransmitter secretion genes were greatly changed, as were many target genes involved in the regulation of FSH secretion. Notably, these are genes that are very closely related to oocyte maturation in growing follicles. We then used pathway analysis for these targeted genes based on the originally selected ten miRNAs. Results indicated that the pathways were mainly related to the biosynthesis of TGF-beta and its signaling pathway, which are very closely related to reproductive system functions.

CONCLUSIONS

Finally, these exosomal miRNAs obtained from PFF may provide a valuable addition to our understanding of the mechanism of porcine oocyte maturation. It is also likely that these exosomal miRNAs could function as molecular biomarkers to choose high-quality oocytes and allow for in vitro porcine embryo production.

LINC00649 underexpression is an adverse prognostic marker in acute myeloid leukemia

BACKGROUND

Long noncoding RNAs (lncRNA) play a role in leukemogenesis, maintenance, development, and therapeutic resistance of AML. While few studies have focused on the prognostic significance of LINC00649 in AML, which we aim to investigate in this present study.

METHODS

We compared the expression level of LINC00649 between AML patients and healthy controls. The Kaplan-Meier curves of AML patients expressing high versus low level of LINC00649 was performed. The LINC00649 correlated genes/miRNAs/lncRNAs and methylation CpG sites were screened by Pearson correlation analysis with R (version 3.6.0), using TCGA-LAML database. The LINC00649 associated ceRNA network was established using lncBase 2.0 and miRWalk 2.0 online tools, combining results from correlation analysis. Finally, a prediction model was constructed using LASSO-Cox regression.

RESULTS

LINC00649 was underexpressed in bone marrow of AML group than that in healthy control group. The patients of LINC00649-low group have significantly inferior PFS and OS. A total of 154 mRNAs, 31 miRNAs, 28 lncRNAs and 1590 methylated CpG sites were identified to be significantly correlated with LINC00649. Furthermore, the network of ceRNA was established with 6 miRNAs and 122 mRNAs. The Lasso-Cox model fitted OS/PFS to novel prediction models, which integrated clinical factors, ELN risk stratification, mRNA/miRNA expression and methylation profiles. The analysis of time-dependent ROC for our model showed a superior AUC (AUC = 0.916 at 1 year, AUC = 0.916 at 3 years, and AUC = 0.891 at 5 years).

CONCLUSIONS

Low expression of LINC00649 is a potential unfavorable prognostic marker for AML patients, which requires the further validation. The analysis by LASSO-COX regression identified a novel comprehensive model with a superior diagnostic utility, which integrated clinical and genetic variables.

Investigating Core Signaling Pathways of Hepatitis B Virus Pathogenesis for Biomarkers Identification and Drug Discovery via Systems Biology and Deep Learning Method

Hepatitis B Virus (HBV) infection is a major cause of morbidity and mortality worldwide. However, poor understanding of its pathogenesis often gives rise to intractable immune escape and prognosis recurrence. Thus, a valid systematic approach based on big data mining and genome-wide RNA-seq data is imperative to further investigate the pathogenetic mechanism and identify biomarkers for drug design. In this study, systems biology method was applied to trim false positives from the host/pathogen genetic and epigenetic interaction network (HPI-GEN) under HBV infection by two-side RNA-seq data. Then, via the principal network projection (PNP) approach and the annotation of KEGG (Kyoto Encyclopedia of Genes and Genomes) pathways, significant biomarkers related to cellular dysfunctions were identified from the core cross-talk signaling pathways as drug targets. Further, based on the pre-trained deep learning-based drug-target interaction (DTI) model and the validated pharmacological properties from databases, i.e., drug regulation ability, toxicity, and sensitivity, a combination of promising multi-target drugs was designed as a multiple-molecule drug to create more possibility for the treatment of HBV infection. Therefore, with the proposed systems medicine discovery and repositioning procedure, we not only shed light on the etiologic mechanism during HBV infection but also efficiently provided a potential drug combination for therapeutic treatment of Hepatitis B.

Overexpression of HOXA10 is associated with unfavorable prognosis of acute myeloid leukemia

Background

HOXA family genes were crucial transcription factors involving cell proliferation and apoptosis. While few studies have focused on HOXA10 in AML. We aimed to investigate the prognostic significance of HOXA10.

Methods

We downloaded datasets from GEO and BeatAML database, to compare HOXA expression level between AML patients and controls. Kaplan-Meier curves were used to estimate the impact of HOXA10 expression on AML survival. The differentially expressed genes, miRNAs, lncRNAs and methylated regions between HOXA10-high and -low groups were obtained using R (version 3.6.0). Accordingly, the gene set enrichment analysis (GSEA) was accomplished using MSigDB database. Moreover, the regulatory TFs/microRNAs/lncRNAs of HOXA10 were identified. A LASSO-Cox model fitted OS to clinical and HOXA10-associated genetic variables by glmnet package.

Results

HOXA10 was overexpressed in AML patients than that in controls. The HOXA10-high group is significantly associated with shorter OS and DFS. A total of 1219 DEGs, 131 DEmiRs, 282 DElncRs were identified to be associated with HOXA10. GSEA revealed that 12 suppressed and 3 activated pathways in HOXA10-high group. Furthermore, the integrated regulatory network targeting HOXA10 was established. The LASSO-Cox model fitted OS to AML-survival risk scores, which included age, race, molecular risk, expression of IKZF2/LINC00649/LINC00839/FENDRR and has-miR-424-5p. The time dependent ROC indicated a satisfying AUC (1-year AUC 0.839, 3-year AUC 0.871 and 5-year AUC 0.813).

Conclusions

Our study identified HOXA10 overexpression as an adverse prognostic factor for AML. The LASSO-COX regression analysis revealed novel prediction model of OS with superior diagnostic utility.

Identification of Known and Novel MicroRNAs in Raspberry Organs Through High-Throughput Sequencing

MicroRNAs (miRNAs) are a class of small endogenous RNAs that play important regulatory roles in plants by negatively affecting gene expression. Studies on the identification of miRNAs and their functions in various plant species and organs have significantly contributed to plant development research. In the current study, we utilized high-throughput sequencing to detect the miRNAs in the root, stem, and leaf tissues of raspberry (Rubus idaeus). A total of more than 35 million small RNA reads ranging in size from 18 to 35 nucleotides were obtained, with 147 known miRNAs and 542 novel miRNAs identified among the three organs. Sequence verification and the relative expression profiles of the six known miRNAs were investigated by stem-loop quantitative real-time PCR. Furthermore, the potential target genes of the known and novel miRNAs were predicted and subjected to Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes pathway annotation. Enrichment analysis of the GO-associated biological processes and molecular functions revealed that these target genes were potentially involved in a wide range of metabolic pathways and developmental processes. Moreover, the miRNA target prediction revealed that most of the targets predicted as transcription factor-coding genes are involved in cellular and metabolic processes. This report is the first to identify miRNAs in raspberry. The detected miRNAs were analyzed by cluster analysis according to their expression, which revealed that these conservative miRNAs are necessary for plant functioning. The results add novel miRNAs to the raspberry transcriptome, providing a useful resource for the further elucidation of the functional roles of miRNAs in raspberry growth and development.

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.

Polymorphisms in miRNA binding sites involved in metabolic diseases in mice and humans

Type 2 diabetes and obesity are well-studied metabolic diseases, which are based on genetic and epigenetic alterations in combination with an obesogenic lifestyle. The aim of this study was to test whether SNPs in miRNA-mRNA binding sites that potentially disrupt binding, elevate the expression of miRNA targets, which participate in the development of metabolic diseases. A computational approach was developed that integrates transcriptomics, linkage analysis, miRNA-target prediction data, and sequence information of a mouse model of obesity and diabetes. A statistical analysis demonstrated a significant enrichment of 566 genes for a location in obesity- and diabetes-related QTL. They are expressed at higher levels in metabolically relevant tissues presumably due to altered miRNA-mRNA binding sites. Of these, 51 genes harbor conserved and impaired miRNA-mRNA-interactions in human. Among these, 38 genes have been associated to metabolic diseases according to the phenotypes of corresponding knockout mice or other results described in the literature. The remaining 13 genes (e.g. Jrk, Megf9, Slfn8 and Tmem132e) could be interesting candidates and will be investigated in the future.

MicroRNA-361-Mediated Inhibition of HSP90 Expression and EMT in Cervical Cancer Is Counteracted by Oncogenic lncRNA NEAT1

Epithelial-mesenchymal transition (EMT) is a key process contributing to cervical cancer (CC) metastasis, and microRNAs (miRNAs) modulate the expression of genes implicated in EMT. However, the accurate role of miR-361 in CC-associated EMT and the mechanisms underlying its function in CC remains largely unknown. The functional roles of miR-361 in CC cells were explored by a series of cell functional assays. Luciferase reporter assays were used to demonstrate the potential interaction between miR-361, HSP90, and long non-coding RNA (lncRNA) NEAT1. We detected a reduction of miR-361 expression in CC tissues compared with normal tissues, and miR-361 overexpression inhibited invasion and EMT phenotypes of CC cells by directly targeting a key EMT activator HSP90. Additionally, we detected significantly higher levels of HSP90 in CC tissues compared with normal tissues, and high expression of HSP90 predicted a poorer prognosis. We further identified NEAT1 as a significantly upregulated lncRNA in CC tissues and high expression of NEAT1 was associated with worse survival in CC patients. NEAT1 directly repressed miR-361 expression and played an oncogenic role in CC cell invasion and sphere formation. Conclusions: These results demonstrated that miR-361 directly targets HSP90 to inhibit the invasion and EMT features, and NEAT1 functions as an oncogenic lncRNA that suppresses miR-361 expression and induces EMT and sphere formation in CC cells, thus providing critical insights into the molecular pathways operating in this malignancy.

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.

A novel computational approach to the silencing of Sugarcane Bacilliform Guadeloupe A Virus determines potential host-derived MicroRNAs in sugarcane (Saccharum officinarum L.)

Sugarcane Bacilliform Guadeloupe A Virus (SCBGAV, genus Badnavirus, family Caulimoviridae) is an emerging, deleterious pathogen of sugarcane which presents a substantial barrier to producing high sugarcane earnings. Sugarcane bacilliform viruses (SCBVs) are one of the main species that infect sugarcane. During the last 30 years, significant genetic changes in SCBV strains have been observed with a high risk of disease incidence associated with crop damage. SCBV infection may lead to significant losses in biomass production in susceptible sugarcane cultivars. The circular, double-stranded (ds) DNA genome of SCBGAV (7.4 Kb) is composed of three open reading frames (ORFs) on the positive strand that replicate by a reverse transcriptase. SCBGAV can infect sugarcane in a semipersistent manner via the insect vectors sugarcane mealybug species. In the current study, we used miRNA target prediction algorithms to identify and comprehensively analyze the genome-wide sugarcane (Saccharum officinarum L.)-encoded microRNA (miRNA) targets against the SCBGAV. Mature miRNA target sequences were retrieved from the miRBase (miRNA database) and were further analyzed for hybridization to the SCBGAV genome. Multiple computational approaches—including miRNA-target seed pairing, multiple target positions, minimum free energy, target site accessibility, maximum complementarity, pattern recognition and minimum folding energy for attachments—were considered by all algorithms. Among them, sof-miR396 was identified as the top effective candidate, capable of targeting the vital ORF3 of the SCBGAV genome. miRanda, RNA22 and RNAhybrid algorithms predicted hybridization of sof-miR396 at common locus position 3394. The predicted sugarcane miRNAs against viral mRNA targets possess antiviral activities, leading to translational inhibition by mRNA cleavage. Interaction network of sugarcane-encoded miRNAs with SCBGAV genes, created using Circos, allow analyze new targets. The finding of the present study acts as a first step towards the creation of SCBGAV-resistant sugarcane through the expression of the identified miRNAs.

Combined proteomics/miRNomics of dendritic cell immunotherapy-treated glioblastoma patients as a screening for survival-associated factors

Glioblastoma is the most prevalent and aggressive brain cancer. With a median overall survival of ~15–20 months under standard therapy, novel treatment approaches are desperately needed. A recent phase II clinical trial with a personalized immunotherapy based on tumor lysate-charged dendritic cell (DC) vaccination, however, failed to prolong survival. Here, we investigated tumor tissue from trial patients to explore glioblastoma survival-related factors. We followed an innovative approach of combining mass spectrometry-based quantitative proteomics (n = 36) with microRNA sequencing plus RT-qPCR (n = 38). Protein quantification identified, e.g., huntingtin interacting protein 1 (HIP1), retinol-binding protein 1 (RBP1), ferritin heavy chain (FTH1) and focal adhesion kinase 2 (FAK2) as factor candidates correlated with a dismal prognosis. MicroRNA analysis identified miR-216b, miR-216a, miR-708 and let-7i as molecules potentially associated with favorable tissue characteristics as they were enriched in patients with a comparably longer survival. To illustrate the utility of integrated miRNomics and proteomics findings, focal adhesion was studied further as one example for a pathway of potential general interest.

Taken together, we here mapped possible drivers of glioblastoma outcome under immunotherapy in one of the largest DC vaccination tissue analysis cohorts so far—demonstrating usefulness and feasibility of combined proteomics/miRNomics approaches. Future research should investigate agents that sensitize glioblastoma to (immuno)therapy—potentially building on insights generated here.

Glioblastoma is an aggressive form of brain cancer and effective immunotherapeutics are limited, with treatment currently based on chemotherapy and radiotherapy. A recent phase II clinical trial tested a personalized, targeted dendritic cell-based immunotherapy but there was no observed improvement in patient survival or progression-free survival compared to standard-of-care therapy. Here, Carmen Visus and colleagues have used tumor tissue samples from glioblastoma patients involved in this trial and receiving immunotherapy. Using a combination of mass spectrometry-based proteomics, microRNA sequencing and RT-qPCR they identified factors associated with survival or poor prognosis. Proteomics associated poor prognosis with various proteins including focal adhesion kinase 2 (FAK2), whilst microRNAs, miR-216b, miR-216a, miR-708 and let-7i, were associated with longer survival. Focussing on one pathway, FAK2, they integrated the proteomic and microRNA datasets and saw a negative association with overall survival across all patients. To test this, they added an FAK inhibitor to glioblastoma cell lines, including cells isolated from trial patients, and observed inhibition of gliomaspheres in treated cells, providing insights into potential immunotherapy targets.

A Supervised Ensemble Approach for Sensitive microRNA Target Prediction

MicroRNAs, a class of small non-coding RNAs, regulate important biological functions via post-transcriptional regulation of messenger RNAs (mRNAs). Despite rapid development in miRNA research, precise experimental methods to determine miRNA target interactions are still lacking. This motivated us to explore the in silico target interaction features and incorporate them in predictive modeling. We propose a systematic approach towards developing a sensitive miRNA target prediction model to explore the interplay of target recognition features. In the first step, we have employed a supervised ensemble under-sampling approach to address the problem of imbalance in the training dataset due to a larger number of negative instances. Various feature selection techniques were evaluated to obtain the optimal feature subset that best recognizes the true miRNA-mRNA targets. In the second step, we have built our optimal model, miRTPred, a novel blending ensemble-based approach that combines the predictions of the best performing traditional and classical ensemble models, through a weighted voting classifier, achieving a sensitivity of 87 percent and F1-score of 0.88 for 3'UTR region of the mRNA transcript. miRTPred outperforms popular machine learning (ML) and non-ML approaches to target prediction algorithms. miRTPred is freely available at http://bicresources.jcbose.ac.in/zhumur/mirtpred.

MicroRNA-1205, encoded on chromosome 8q24, targets EGLN3 to induce cell growth and contributes to risk of castration-resistant prostate cancer

The chromosome 8q24.21 locus, which contains the proto-oncogene c-MYC, long non-coding RNA PVT1, and microRNAs (miRs), is the most commonly amplified region in human prostate cancer. A long-range interaction of genetic variants with c-MYC or long non-coding PVT1 at this locus contributes to the genetic risk of prostate cancer. At this locus is a cluster of genes for six miRs (miR-1204, −1205, −1206, −1207–3p, −1207–5p, and −1208), but their functional role remains elusive. Here, the copy numbers and expressions of miRs-1204~1208 were investigated using quantitative PCR for prostate cancer cell lines and primary tumors. The data revealed that copy numbers and expression of miR-1205 were increased in both castration-resistant prostate cancer cell lines and in primary tumors. In castration-resistant prostate cancer specimens, the copy number at the miR-1205 locus correlated with expression of miR-1205. Furthermore, functional analysis with an miR-1205 mimic, an miR-1205 inhibitor, and CRISPR/Cas9 knockout revealed that, in human prostate cancer cells, miR-1205 promoted cell proliferation and cell cycle progression and inhibited hydrogen peroxide-induced apoptosis. In these cells, miR-1205 downregulated expression of the Egl-9 family hypoxia inducible factor 3 (EGLN3) gene and targeted a site in its 3’-untranslated region to downregulate its transcriptional activity. Thus, by targeting EGLN3, miR-1205 has an oncogenic role and may contribute to the genetic risk of castration-resistant prostate cancer.

MiR-34a and miR-34b/c have distinct effects on the suppression of lung adenocarcinomas

Three miR-34 family members (miR-34a, miR-34b, and miR-34c) are clustered on two different chromosomal loci, Mir34a and Mir34b/c. These miRNAs have identical seed sequences, which are predicted to target the same set of genes. However, miR-34a and miR-34c have different sets of negatively correlated genes in lung adenocarcinoma data from The Cancer Genome Atlas. Therefore, we hypothesized that the individual miR-34 family members, which are tumor suppressive miRNAs, would have varying effects on lung tumorigenesis. To show this, we overexpressed each miR-34 cluster in murine lung cancer cells. MiR-34b/c enhanced cancer cell attachment and suppressed cell growth and invasion compared with miR-34a. In a syngeneic mouse model, both miR-34a and miR-34b/c blocked lung metastasis. However, miR-34b/c suppressed tumor growth more than miR-34a. MiR-34b/c also decreased the expression of mesenchymal markers (Cdh2 and Fn1) and increased the expression of epithelial markers (Cldn3, Dsp, and miR-200) to a greater degree than miR-34a. These results imply that miR-34b and miR-34c inhibit epithelial-to-mesenchymal transition. Furthermore, knockout of all three miR-34 members promoted mutant Kras-driven lung tumor progression in mice. Similarly, lung adenocarcinoma patients with higher miR-34a/b/c levels had better survival rates than did those with lower levels. In summary, we suggest that miR-34b and miR-34c are more effective tumor suppressors than miR-34a.

Exploring the effects of three similar small RNA molecules called micro-RNAs (miRNAs) that can restrict the activity of specific genes reveals how they might be used in cancer treatment. RNA is best known as messenger RNA, which carries a copy of a gene’s information into the cell cytoplasm to direct protein manufacture. Many small RNAs play less well-known but crucial roles by binding to messenger RNA molecules to regulate their activity. Researchers in South Korea and USA, led by Young-Ho Ahn at Ewha Womans University in Seoul, investigated how these miRNAs can suppress lung cancer in mice. Their results reveal details of how the miRNAs inhibit the expression of specific tumor-supporting genes. They suggest that three of the RNAs administered together might treat cancer more effectively than using only one as in previous trials.

miRandb: A Metadatabase of Online Resources of miRNA and miRNA Targets

MicroRNA (miRNA) studies deliver numerous types of information including miRNA identification, sequence of miRNAs, target prediction, roles in diseases, and interactions in signaling pathways. Considering the different types of miRNA data, the number of miRNA databases has been increasing quickly. While resources have been planned to simplify miRNA analysis, scientists are facing the challenging task of choosing the most proper tool to retrieve related information. In this chapter, we introduce the use of miRandb, a resource that we have established to present an outline of different types of miRNA online resources and to simplify finding the right miRNA information that scientists need for their research. miRandb offers a user-friendly platform to find related information about any miRNA data among more than 188 present miRNA databases. miRandb has an easy procedure, and information can be retrieved by miRNA category resources. Each database comprises numerous kinds of information including database activity, description, main and unique features, organism, URL, publication, category, published year, citations per year, last update, and relative popularity. miRandb provides several opportunities and facilitates access to diverse classes of microRNA resources. miRandb is available at http://miRandb.ir .

miRNAs in Insects Infected by Animal and Plant Viruses

Viruses vectored by insects cause severe medical and agricultural burdens. The process of virus infection of insects regulates and is regulated by a complex interplay of biomolecules including the small, non-coding microRNAs (miRNAs). Considered an anomaly upon its discovery only around 25 years ago, miRNAs as a class have challenged the molecular central dogma which essentially typifies RNAs as just intermediaries in the flow of information from DNA to protein. miRNAs are now known to be common modulators or fine-tuners of gene expression. While recent years has seen an increased emphasis on understanding the role of miRNAs in host-virus associations, existing literature on the interaction between insects and their arthropod-borne viruses (arboviruses) is largely restricted to miRNA abundance profiling. Here we analyse the commonalities and contrasts between miRNA abundance profiles with different host-arbovirus combinations and outline a suggested pipeline and criteria for functional analysis of the contribution of miRNAs to the insect vector-virus interaction. Finally, we discuss the potential use of the model organism, Drosophila melanogaster, in complementing research on the role of miRNAs in insect vector-virus interaction.

Impact of miRNA-mRNA Profiling and Their Correlation on Medulloblastoma Tumorigenesis

Medulloblastoma (MB) is a clinically challenging, childhood brain tumor with a diverse genetic makeup and differential miRNA profile. Aiming to identify deregulated miRNAs in MB, the miRNA expression profile of human MB samples was compared to that of normal cerebellar tissues. As a result, 8 upregulated and 64 downregulated miRNAs were identified in MB samples. Although various algorithms have been developed to predict the interaction between miRNA-mRNA pairs, the complexity and fidelity of miRNA-mRNA remain a concern. Therefore, to identify the signatures of miRNA-mRNA interactions essential for MB pathogenesis, miRNA profiling, RNA sequencing, and ingenuity pathway analysis (IPA) were performed in the same primary human MB samples. Further, when miR-217 was inhibited, a significant upregulation of predicted target genes SIRT1, ROBO1, FOXO3, and SMAD7 in HDMB03 cells was observed, confirming the validity of our approach. Functional analysis revealed that the inhibition of miR-217 in HDMB03 cells suppresses colony formation, migration, invasion, promoted apoptosis, and arrested cell population in S phase, indicating that manipulation of miR-217 may have a therapeutic potential for MB patients. Therefore, our study provides an essential platform for future investigations of specific miRNAs responsible for MB pathogenesis.

Peripheral blood microRNAs expression is associated with infant respiratory syncytial virus infection

MicroRNAs respond to the inflammatory responses induced by RNA virus infection. In this study, we investigated the specific microRNA profile in the peripheral blood of infants infected with respiratory syncytial virus (RSV). Blood specimens were analyzed using microRNA microarrays, followed by quantitative RT-PCR. A specific microRNA profile in the peripheral blood of RSV-infected infants was identified for the first time. MiR-106b-5p, miR-20b-5p, and miR-342-3p were upregulated, while miR-320e, miR-320d, miR-877-5p, miR-122-5p, and miR-92b-5p were downregulated. Pathway analysis indicated that the dysregulated microRNAs were involved in inflammatory and immune responses, including Wnt, TGF-β, insulin, and T and B cell receptor signaling. These results demonstrate that RSV infection associates with a distinct microRNA fingerprint and suggest that RSV induces inflammatory responses in infants.

Construction of miRNA-miRNA networks revealing the complexity of miRNA-mediated mechanisms in trastuzumab treated breast cancer cell lines

Trastuzumab is a monoclonal antibody frequently used to prevent the progression of HER2+ breast cancers, which constitute approximately 20% of invasive breast cancers. microRNAs (miRNAs) are small, non-coding RNA molecules that are known to be involved in gene regulation. With their emerging roles in cancer, they are recently promoted as potential candidates to mediate therapeutic actions by targeting genes associated with drug response. In this study we explored miRNA-mediated regulation of trastuzumab mechanisms by identifying the important miRNAs responsible for the drug response via homogenous network analysis. Our network model enabled us to simplify the complexity of miRNA interactions by connecting them through their common pathways. We outlined the functionally relevant miRNAs by constructing pathway-based miRNA-miRNA networks in SKBR3 and BT474 cells, respectively. Identification of the most targeted genes revealed that trastuzumab responsive miRNAs favourably regulate the repression of targets with longer 3’UTR than average considered to be key elements, while the miRNA-miRNA networks highlighted central miRNAs such as hsa-miR-3976 and hsa-miR-3671 that showed strong interactions with the remaining members of the network. Furthermore, the clusters of the miRNA-miRNA networks showed that trastuzumab response was mostly established through cancer related and metabolic pathways. hsa-miR-216b was found to be the part of the most powerful interactions of metabolic pathways, which was defined in the largest clusters in both cell lines. The network based representation of miRNA-miRNA interactions through their shared pathways provided a better understanding of miRNA-mediated drug response and could be suggested for further characterization of miRNA functions.

In silico target network analysis of de novo-discovered, tick saliva-specific microRNAs reveals important combinatorial effects in their interference with vertebrate host physiology

The hard tick Ixodes ricinus is an important disease vector whose salivary secretions mediate blood-feeding success on vertebrate hosts, including humans. Here we describe the expression profiles and downstream analysis of de novo-discovered microRNAs (miRNAs) expressed in I. ricinus salivary glands and saliva. Eleven tick-derived libraries were sequenced to produce 67,375,557 Illumina reads. De novo prediction yielded 67 bona fide miRNAs out of which 35 are currently not present in miRBase. We report for the first time the presence of microRNAs in tick saliva, obtaining furthermore molecular indicators that those might be of exosomal origin. Ten out of these microRNAs are at least 100 times more represented in saliva. For the four most expressed microRNAs from this subset, we analyzed their combinatorial effects upon their host transcriptome using a novel in silico target network approach. We show that only the inclusion of combinatorial effects reveals the functions in important pathways related to inflammation and pain sensing. A control set of highly abundant microRNAs in both saliva and salivary glands indicates no significant pathways and a far lower number of shared target genes. Therefore, the analysis of miRNAs from pure tick saliva strongly supports the hypothesis that tick saliva miRNAs can modulate vertebrate host homeostasis and represents the first direct evidence of tick miRNA-mediated regulation of vertebrate host gene expression at the tick-host interface. As such, the herein described miRNAs may support future drug discovery and development projects that will also experimentally question their predicted molecular targets in the vertebrate host.

Novel miRNA-mRNA interactions conserved in essential cancer pathways

Cancer is a complex disease in which unrestrained cell proliferation results in tumour development. Extensive research into the molecular mechanisms underlying tumorigenesis has led to the characterization of oncogenes and tumour suppressors that are key elements in cancer growth and progression, as well as that of other important elements like microRNAs. These genes and miRNAs appear to be constitutively deregulated in cancer. To identify signatures of miRNA-mRNA interactions potentially conserved in essential cancer pathways, we have conducted an integrative analysis of transcriptomic data, also taking into account methylation and copy number alterations. We analysed 18,605 raw transcriptome samples from The Cancer Genome Atlas covering 15 of the most common types of human tumours. From this global transcriptome study, we recovered known cancer-associated miRNA-targets and importantly, we identified new potential targets from miRNA families, also analysing the phenotypic outcomes of these genes/mRNAs in terms of survival. Further analyses could lead to novel approaches in cancer therapy.

Perfluorooctanoic acid affects endocytosis involving clathrin light chain A and microRNA-133b-3p in mouse testes

Perfluorooctanoic acid (PFOA) is an abundant perfluoroalkyl substance widely applied in industrial and consumer products. Among its potential health hazards, testicular toxicity is of major concern. To explore the potential effect of miRNA on post-translational regulation after PFOA exposure, changes in miRNAs were detected via miRNA array. Seventeen miRNAs were differentially expressed (eight upregulated, nine downregulated) in male mouse testes after exposure to 5mg/kg/d of PFOA for 28d (>1.5-fold and P<0.05 compared with the control). Eight of these miRNAs were further selected for TaqMan qPCR analysis. Proteomic profile analysis indicated that many changed proteins after PFOA treatment, including intersectin 1 (ITSN1), serine protease inhibitor A3K (Serpina3k), and apolipoprotein a1 (APOA1), were involved in endocytosis and blood-testis barrier (BTB) processes. These changes were further verified by immunohistochemical and Western blot analyses. Endocytosis-related genes were selected for qPCR analysis, with many found to be significantly changed after PFOA treatment, including epidermal growth factor receptor pathway substrate 8 (Eps8), Eps15, cortactin, cofilin, espin, vinculin, and zyxin. We further predicted the potential interaction between changed miRNAs and proteins, which indicated that miRNAs might play a role in the post-translational regulation of gene expression after PFOA treatment in mouse testes. Among them, miR-133b-3p/clathrin light chain A (CLTA) was selected and verified in vitro by transfection and luciferase activity assay. Results showed that PFOA exposure affects endocytosis in mouse testes and that CLTA is a potential target of miR-133b-3p.

Prediction of miRNA-mRNA Interactions Using miRGate

miRGate ( http://mirgate.bioinfo.cnio.es /) is a freely available database that contains predicted and experimentally validated microRNA-messenger RNA (miRNA-mRNA) target pairs. This resource includes novel predictions from five well-established algorithms, but recalculated from a common and comprehensive sequence dataset. It includes all 3'-UTR sequences of all known genes of the three more widely employed genomes (human, mouse, and rat), and all annotated miRNA sequences from those genomes. Besides, it also contains predictions for all genes in human targeted by miRNA viruses such as Epstein-Barr and Kaposi sarcoma-associated herpes virus.The approach intends to circumvent one of the main drawbacks in this area, as diverse sequences and gene database versions cause poor overlap among different target prediction methods even with experimentally confirmed targets. As a result, miRGate predictions have been successfully validated using functional assays in several laboratories.This chapter describes how a user can access target information via miRGate's web interface. It also shows how automatically access the database through the programmatic interface based on representational state transfer services (REST), using the application programming interface (API) available at http://mirgate.bioinfo.cnio.es/API .

A Comprehensive Review on the Genetic Regulation of Cisplatin-induced Nephrotoxicity

Cisplatin (CDDP) is a well-known antineoplastic drug which has been extensively utilized over the last decades in the treatment of numerous kinds of tumors. However, CDDP induces a wide range of toxicities in a dose-dependent manner, among which nephrotoxicity is of particular importance. Still, the mechanism of CDDP-induced renal damage is not completely understood; moreover, the knowledge about the role of microRNAs (miRNAs) in the nephrotoxic response is still unknown. miRNAs are known to interact with the representative members of a diverse range of regulatory pathways (including postnatal development, proliferation, inflammation and fibrosis) and pathological conditions, including kidney diseases: polycystic kidney diseases (PKDs), diabetic nephropathy (DN), kidney cancer, and drug-induced kidney injury. In this review, we shed light on the following important aspects: (i) information on genes/proteins and their interactions with previously known pathways engaged with CDDP-induced nephrotoxicity, (ii) information on newly discovered biomarkers, especially, miRNAs for detecting CDDP-induced nephrotoxicity and (iii) information to improve our understanding on CDDP. This information will not only help the researchers belonging to nephrotoxicity field, but also supply an indisputable help for oncologists to better understand and manage the side effects induced by CDDP during cancer treatment. Moreover, we provide up-to-date information about different in vivo and in vitro models that have been utilized over the last decades to study CDDP-induced renal injury. Taken together, this review offers a comprehensive network on genes, miRNAs, pathways and animal models which will serve as a useful resource to understand the molecular mechanism of CDDP-induced nephrotoxicity.