microRNA Target Prediction Research Articles

Overexpression of miR-124-3p affects zebrafish inner ear development and hearing function via downregulation of EYA1 gene expression

The EYA1 gene is essential for normal inner ear development and affects the development and function of the inner ear in a dose-dependent manner. However, the mechanisms regulating EYA1 gene expression are not well understood. Recently, miRNAs have become recognized as important regulators of gene expression. In this study, we identified miR-124-3p through a microRNA (miRNA) target prediction website and found that miR-124-3p and its target site in the EYA1 3′ untranslated region (3′UTR) are conserved in most vertebrates. Both in vivo and in vitro, the interaction of miR-124-3p with the EYA1 3′UTR exerts a negative regulatory effect. Microinjection of agomiR-124-3p into zebrafish embryos resulted in a phenotype of reduced auricular area, suggesting inner ear dysplasia. In addition, injection of agomiR-124-3p or antagomiR-124-3p caused abnormal hearing function in zebrafish. In conclusion, our results suggest that miR-124-3p can affect inner ear development and hearing function in zebrafish by regulating EYA1.

Coffee-Derived Exosome-Like Nanoparticles: Are They the Secret Heroes?

Regular coffee consumption has beneficial and preventative effects on liver and chronic neurodegenerative diseases. However, the studies performed with the ingredients found in coffee beverages have not clarified the responsible mechanisms. Exosomes are small, membrane-coated cargo packages secreted by prokaryote and eukaryote cells. Exosomes regulate intercellular communication and affect cellular metabolic activities even among different species. In this study, we aimed to isolate and characterize the edible plant-derived exosome-like nanoparticles from roasted hot coffee beverages, hypothesizing that the edible plant-derived exosome-like nanoparticles were responsible for the beneficial effects of coffee. Size exclusion chromatography and commercial kits were used for the isolation process. Efficient coffee edible plant-derived exosome-like nanoparticle fractions were determined by an ultraviolet-visible spectrophotometer. Harvested coffee edible plant-derived exosome-like nanoparticles were characterized by transmission electron microscopy. The quantification procedure was performed using a commercial kit. Coffee edible plant-derived exosome-like nanoparticles' proliferative effects on human hepatic stellate cells and human hepatocellular carcinoma cells were studied using an MTT (3-(4,5-Dimethylthiazol-2-yl)-2,5-Diphenyltetrazolium Bromide) assay. Whole-exosome RNA sequencing was performed. Transmission electron microscopy scanning analysis indicated round-shaped nanoparticles with sizes ranging from 40 to 100 nm. Both size exclusion chromatography and kit-isolated edible plant-derived exosome-like nanoparticle samples showed maximum absorbance at 227.5 nm in ultraviolet-visible spectrophotometer analysis. Regarding the quantitation results, kit isolation was more efficient than the size exclusion chromatography method when the harvested particle numbers were compared. An important MTT assay finding confirmed the observed beneficial effects of coffee beverages: coffee edible plant-derived exosome-like nanoparticles significantly suppressed hepatocellular carcinoma cell proliferation. As a result of sequencing, we identified 15 mature miRNAs. A MapReduce-based MicroRNA Target Prediction Method (The DIANA tools' MR-microT algorithm) highlighted 2 genes specifically associated with the miRNAs that we obtained: KMT2C and ZNF773. For the first time in the literature, coffee edible plant-derived exosome-like nanoparticles were identified. These nanoparticles may have therapeutic effects on chronic liver diseases. Experimental studies, therefore, should be performed on disease models to demonstrate their efficacy.

CircRNA, a novel diagnostic biomarker for coronary heart disease.

This study aimed to identify the potential diagnostic biomarkers of coronary heart disease (CHD) from exosome-derived circRNA. The microarray data of circRNA derived from the exosomes of patients with CHD and mRNA in acute myocardial infarction was retrieved from exoRBase website and GEO database (GSE61144), respectively, to identify the differentially expressed genes (DEGs). Our findings detected the differentially expressed circRNAs and mRNAs and predicted their correlation with microRNAs using the microRNA target prediction website, thus ascertaining the corresponding circ-microRNA and micro-mRNAs. Then, we performed systematic Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis on the differentially expressed mRNA. Protein-Protein Interactions (PPI) of these DEGs were examined using STRING. The receiver operator characteristic (ROC) curve was used to validate the diagnostic efficacy of circRNA in patients with CHD. Finally, the RNAs identified in this study were verified by quantitative real-time polymerase chain reaction (qRT-PCR). A total of 85 differentially expressed circRNAs (4 up-regulated and 81 down-regulated) were identified by screening the circRNAs in exosome of CHD patients. Based on the prediction data of circRNA, mRNA, and the corresponding microRNA, a ceRNA network was constructed, including 7 circRNA nodes, 5 microRNA nodes, and 2 mRNA nodes. Finally, validated by qRT-PCR testing, we found circRNA0001785, circRNA0000973, circRNA0001741, and circRNA0003922 to be the promising candidate for the effective prediction of CHD. These potential diagnostic markers can provide insight for further research on the occurrence of CHD or even acute coronary syndrome (ACS).

MicroRNA Identification, Target Prediction, and Validation for Crop Improvement.

Micro-RNAs (mi-RNAs) are regulatory elements that play a vital role in the growth, development, and metabolic regulation of plants. In current research, the isolation of miRNAs is a tedious and difficult task using in vitro methods. However, recent exploration into the remarkably highly conserved nature of nucleotide sequences of miRNAs assists in the identification of miRNAs in plant species through homologous approaches. Here, we describe the in silico-based method for identification of miRNAs from the EST database which is emerging as a faster and more reliable approach along with the development of miRNA-SSR markers. This approach has the potential to accelerate research into the regulation of gene expression in various plant species such as tea, potato, tomato, tobacco, and orphan crops like cluster bean.

Detection of features predictive of microRNA targets by integration of network data.

Gene activity is controlled by multiple molecular mechanisms, for instance through transcription factors or by microRNAs (miRNAs), among others. Established bioinformatics tools for the prediction of miRNA target genes face the challenge of ensuring accuracy, due to high false positive rates. Further, these tools present poor overlap. However, we demonstrated that it is possible to filter good predictions of miRNA targets from the bulk of all predictions by using information from the gene regulatory network. Here, we take advantage of this strategy that selects a large subset of predicted microRNA binding sites as more likely to possess less false-positives because of their over-representation in RE1 silencing transcription factor (REST)-regulated genes from the background of TargetScanHuman 7.2 predictions to identify useful features for the prediction of microRNA targets. These enriched miRNA families would have silencing activity for neural transcripts overlapping the repressive activity on neural genes of REST. We analyze properties of associated microRNA binding sites and contrast the outcome to the background. We found that the selected subset presents significant differences respect to the background: (i) lower GC-content in the vicinity of the predicted miRNA binding site, (ii) more target genes with multiple identical microRNA binding sites and (iii) a higher density of predicted microRNA binding sites close to the 3’ terminal end of the 3’-UTR. These results suggest that network selection of miRNA-mRNA pairs could provide useful features to improve microRNA target prediction.

MiR-30e-5p attenuates neuronal deficit and inflammation of rats with intracerebral hemorrhage by regulating TLR4.

microRNAs (miRNAs or miRs) have been reported to regulate the pathology of intracerebral hemorrhage (ICH). Therefore, the present study aimed to investigate the function of miR-30e-5p in rats with ICH with specific focus on Toll-like receptor (TLR)4. In the present study, collagenase type IV was used for the establishment of the ICH model in rats, prior to which the rats were injected with miR-30e-5p mimic or miR-30e-5p mimic + pcDNA3.1-TLR4 plasmid. The expression levels of miR-30e-5p and TLR4 were then measured using reverse transcription-quantitative PCR and western blotting. The potential interaction between miR-30e-5p and TLR4 was tested using the MicroRNA Target Prediction Database and dual-luciferase reporter and RNA immunoprecipitation assay. In addition, the concentration of TNF-α, IL-6 and IL-1β was measured using ELISA. The protein expression levels of TLR4/myeloid differentiation factor 88 (MyD88)/TIR-domain-containing adapter-inducing interferon-β (TRIF) signaling-associated molecules were measured by western blotting. Following induction of ICH, miR-30e-5p expression was downregulated, while TLR4 expression was upregulated. By contrast, injection with miR-30e-5p mimic rescued neuronal function while suppressing neuronal inflammation in rats following ICH; these effects were reversed by co-overexpression of TLR4. Furthermore, overexpression of miR-30e-5p inactivated TLR4/MyD88/TRIF signaling in rats with ICH; this was also reversed by overexpression of TLR4. Taken together, these results suggested that overexpression of miR-30e-5p exerted a protective role against neuronal deficit and inflammation caused by ICH in rats by targeting TLR4 and inactivating TLR4/MyD88/TRIF signaling.

MIO: microRNA target analysis system for immuno-oncology.

SummaryMicroRNAs have been shown to be able to modulate the tumor microenvironment and the immune response and hence could be interesting biomarkers and therapeutic targets in immuno-oncology; however, dedicated analysis tools are missing. Here, we present a user-friendly web platform MIO and a Python toolkit miopy integrating various methods for visualization and analysis of provided or custom bulk microRNA and gene expression data. We include regularized regression and survival analysis and provide information of 40 microRNA target prediction tools as well as a collection of curated immune related gene and microRNA signatures and processed TCGA data including estimations of infiltrated immune cells and the immunophenoscore. The integration of several machine learning methods enables the selection of prognostic and predictive microRNAs and gene interaction network biomarkers.Availability and implementation https://mio.icbi.at, https://github.com/icbi-lab/mio and https://github.com/icbi-lab/miopy.Supplementary information Supplementary data are available at Bioinformatics online.

ScanMiR: a biochemically based toolkit for versatile and efficient microRNA target prediction.

microRNAs are important post-transcriptional regulators of gene expression, but the identification of functionally relevant targets is still challenging. Recent research has shown improved prediction of microRNA-mediated repression using a biochemical model combined with empirically-derived k-mer affinity predictions; however, these findings are not easily applicable. We translate this approach into a flexible and user-friendly bioconductor package, scanMiR, also available through a web interface. Using lightweight linear models, scanMiR efficiently scans for binding sites, estimates their affinity and predicts aggregated transcript repression. Moreover, flexible 3'-supplementary alignment enables the prediction of unconventional interactions, such as bindings potentially leading to target-directed microRNA degradation or slicing. We showcase scanMiR through a systematic scan for such unconventional sites on neuronal transcripts, including lncRNAs and circRNAs. Finally, in addition to the main bioconductor package implementing these functions, we provide a user-friendly web application enabling the scanning of sequences, the visualization of predicted bindings and the browsing of predicted target repression. scanMiR and companion packages are implemented in R, released under the GPL-3 and accessible on Bioconductor (https://bioconductor.org/packages/release/bioc/html/scanMiR.html) as well as through a shiny web server (https://ethz-ins.org/scanMiR/). Supplementary data are available at Bioinformatics online.

The Prognostic Value of the Developmental Gene FZD6 in Young Saudi Breast Cancer Patients: A Biomarkers Discovery and Cancer Inducers OncoScreen Approach.

Wnt signalling receptors, Frizzleds (FZDs), play a pivotal role in many cellular events during embryonic development and cancer. Female breast cancer (BC) is currently the worldwide leading incident cancer type that cause 1 in 6 cancer-related death. FZD receptors expression in cancer was shown to be associated with tumour development and patient outcomes including recurrence and survival. FZD6 received little attention for its role in BC and hence we analysed its expression pattern in a Saudi BC cohort to assess its prognostic potential and unravel the impacted signalling pathway. Paraffin blocks from approximately 405 randomly selected BC patients aged between 25 and 70 years old were processed for tissue microarray using an automated tissue arrayer and then subjected to FZD6 immunohistochemistry staining using the Ventana platform. Besides, Ingenuity Pathway Analysis (IPA) knowledgebase was used to decipher the upstream and downstream regulators of FZD6 in BC. TargetScan and miRabel target-prediction databases were used to identify the potential microRNA to regulate FZD6 expression in BC. Results showed that 60% of the BC samples had a low expression pattern while 40% showed a higher expression level. FZD6 expression analysis showed a significant correlation with tumour invasion (p < 0.05), and borderline significance with tumour grade (p = 0.07). FZD6 expression showed a highly significant association with the BC patients’ survival outcomes. This was mainly due to the overall patients’ cohort where tumours with FZD6 elevated expression showed higher recurrence rates (DFS, p < 0.0001, log-rank) and shorter survival times (DSS, p < 0.02, log-rank). Interestingly, the FZD6 prognostic value was more potent in younger BC patients as compared to those with late onset of the disease. TargetScan microRNA target-prediction analysis and validated by miRabel showed that FZD6 is a potential target for a considerable number of microRNAs expressed in BC. The current study demonstrates a potential prognostic role of FZD6 expression in young BC female patients and provides a better understanding of the involved molecular silencing machinery of the Wnt/FZD6 signalling. Our results should provide a better understanding of FZD6 role in BC by adding more knowledge that should help in BC prevention and theranostics.

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. Highthroughput experimental approaches for miRNA target identification are costly and timeconsuming, depending on various factors. It is vitally important to develop 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 especially 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.

MicroRNA expression signature and target prediction in familial and sporadic primary macronodular adrenal hyperplasia (PMAH)

BackgroundPrimary macronodular adrenal hyperplasia (PMAH), previously termed ACTH-independent macronodular adrenal hyperplasia (AIMAH), is a rare cause of Cushing’s syndrome usually characterized by functioning adrenal macronodules and increased cortisol production.MethodsTo screen and analyse the microRNA (miRNA) profile of PMAH in order to elucidate its possible pathogenesis, a miRNA microarray was used to test tissue samples from patients with familial PMAH, patients with sporadic PMAH and normal control samples of other nontumour adrenocortical tissues and identify characteristic microRNA expression signatures. Randomly selected miRNAs were validated by quantitative real-time reverse transcription polymerase chain reaction (qRT-PCR). Furthermore, the key signalling pathways and miRNAs involved in PMAH pathogenesis were determined by gene ontology and pathway analysis.ResultsCharacteristic microRNA expression signatures were identified for patients with familial PMAH (16 differentially expressed microRNAs) and patients with sporadic PMAH (8 differentially expressed microRNAs). The expression of the selected miRNAs was confirmed by qRT-PCR, suggesting the high reliability of the miRNA array analysis results. Pathway analysis showed that the most enriched pathway was the renal cell carcinoma pathway. Overexpression of miR-17, miR-20a and miR-130b may inhibit glucocorticoid-induced apoptosis in PMAH pathogenesis.ConclusionWe identified the miRNA signatures in patients with familial and sporadic PMAH. The differentially expressed miRNAs may be involved in the mechanisms of PMAH pathogenesis. Specific miRNAs, such as miR-17, miR-20a and miR-130b, may be new targets for further functional studies of PMAH.

Recent Development of Bioinformatics Tools for microRNA Target Prediction

Recent Development of Bioinformatics Tools for microRNA Target Prediction

MicroRNA-152 Regulates Endometrial Serous Carcinoma Cell Motility by Suppressing Matrix Metalloproteinase 10 Expression.

MicroRNA-152 (miR-152) expression has been reported to be associated with poor prognosis in patients with endometrial serous carcinoma (ESC). However, the function of miR-152 in ESCs is not fully understood. The present study aimed to investigate the involvement of miR-152 in ESC progression. The influence of miR-152 overexpression on cell proliferation and motility was assessed by transfecting two human ESC cell lines, USPC-1 and SPAC-1-L, with a miR-152 precursor. MiR-152 overexpression increased apoptosis and inhibited the proliferation of the two ESC cell lines. Cell motility was also suppressed in both cell lines following precursor transfection. Conversely, miR-152 inhibitor transfection led to an increase in cell migration ability, suggesting the involvement of miR-152 in ESC cell motility. Results of the analysis of publicly available messenger RNA dataset indicated that high expression of matrix metalloproteinase 10 (MMP10), one of the predicted targets of miR-152 by microRNA target prediction database, was a poor prognostic factor for ESC. In vitro examination results revealed that miR-152 overexpression reduced MMP10 expression, and knockdown of MMP10 significantly reduced cell motility. This study elucidates the function of miR-152 as a tumor suppressor in ESCs. We demonstrated that miR-152 plays an important role in ESC cell motility by regulating MMP10 expression.

Machine Learning Based Methods and Best Practices of microRNA-Target Prediction and Validation.

Within the last years, more and more noncoding RNAs (ncRNAs) became the focal point to understand cell regulatory mechanisms because one class of ncRNAs, microRNAs (miRNAs), plays an essential role in translation repression or degradation of specific mRNAs and is implicated in disease etiology. miRNAs can serve as oncomiRs (oncogenic miRNAs) and tumor suppressor miRNAs, thus, miRNA therapeutics in clinical trials have become a vital component with respect to cancer treatment. To circumvent side-effects and allow an accurate effect it is crucial to accurately predict miRNAs and their mRNA targets. Over the last two decades, different approaches for miRNA prediction as well as miRNA target prediction have been developed and improved based on sequence and structure features. Nowadays, the abundance of high-throughput sequencing data and databases of miRNAs and miRNA targets from different species allow the training, testing, and validation of predicted miRNAs and miRNA targets with machine learning methods. This book chapter focuses on the important requirements for miRNA target prediction tools using ML like common features used for miRNA-binding site prediction. Furthermore, best practices for the prediction and validation of miRNA-mRNA targets are presented and set in the context of possible applications for cancer diagnosis and therapeutics.

MiR-541-3p suppresses gastric cancer via negative regulation of HSF1

Purpose: To explore the effects of miR-541-3P on the expression of heat shock transcription factor 1 (HSF1) in gastric cancer cells (GC).Methods: The MicroRNA Target Prediction Database was used to predict whether miR-541-3p interacts with HSF1. Interaction was assessed by dual-luciferase reporter assays. Furthermore, miR-541-3p mRNA levels in GC cell lines were determined by qRT-PCR. Human GC cell lines MKN45 and NCI-N87 were transfected with miR-541-3p mimic. Cell apoptosis, proliferation, invasion, and migration were evaluated using flow cytometry, apoptosis assays, Edu assays, CCK-8 assays, and transwell assays, respectively. Caspase-3, Bcl-2, and cleaved caspase-3 expression levels were determined by western blot.Results: Expression of miR-541-3p was significantly down-regulated in GC cells. Functionally, miR-541-3p mimic inhibited GC cell proliferation, migration, and invasion and induced apoptosis in vitro (p &lt;0.01). Mechanistically, miR-541-3p interacted with HSF1 and inhibited its expression. Overexpression of HSF1 counteracted the effects of miR-541-3p mimic in GC cells.Conclusion: These results indicate that miR-541-3p suppresses the development of GC by targeting HSF1 and thus, is a possible strategy for for the management of GC.

OncomiR miR-182-5p Enhances Radiosensitivity by Inhibiting the Radiation-Induced Antioxidant Effect through SESN2 in Head and Neck Cancer.

Radiotherapy is routinely used for the treatment of head and neck squamous cell carcinoma (HNSCC). However, the therapeutic efficacy is usually reduced by acquired radioresistance and locoregional recurrence. In this study, The Cancer Genome Atlas (TCGA) analysis showed that radiotherapy upregulated the miR-182/96/183 cluster and that miR-182 was the most significantly upregulated. Overexpression of miR-182-5p enhanced the radiosensitivity of HNSCC cells by increasing intracellular reactive oxygen species (ROS) levels, suggesting that expression of the miR-182 family is beneficial for radiotherapy. By intersecting the gene targeting results from three microRNA target prediction databases, we noticed that sestrin2 (SESN2), a molecule resistant to oxidative stress, was involved in 91 genes predicted in all three databases to be directly recognized by miR-182-5p. Knockdown of SESN2 enhanced radiation-induced ROS and cytotoxicity in HNSCC cells. In addition, the radiation-induced expression of SESN2 was repressed by overexpression of miR-182-5p. Reciprocal expression of the miR-182-5p and SESN2 genes was also analyzed in the TCGA database, and a high expression of miR-182-5p combined with a low expression of SESN2 was associated with a better survival rate in patients receiving radiotherapy. Taken together, the current data suggest that miR-182-5p may regulate radiation-induced antioxidant effects and mediate the efficacy of radiotherapy.

MicroRNA Expression Profiling in Porcine Liver, Jejunum and Serum upon Dietary DON Exposure Reveals Candidate Toxicity Biomarkers.

Deoxynivalenol (DON), a frequent mycotoxin worldwide, impairs human and animal health. The response of microRNAs, small non-coding RNAs, to DON has been scarcely investigated, but holds remarkable potential for biomarker applications. Hence, we aimed to investigate DON-induced changes in the microRNA expression in porcine liver, jejunum and serum by combining targeted and untargeted analyses. Piglets received uncontaminated feed or feed containing 900 µg/kg and 2500 µg/kg DON for four weeks, followed by a wash-out period. In tissue, only slight changes in microRNA expression were detected, with ssc-miR-10b being downregulated in liver of DON-exposed piglets. In serum, several microRNAs were differentially expressed upon DON exposure, four of which were validated by qPCR (ssc-miR-16, ssc-miR-128, ssc-miR-451, ssc-miR-205). The serum microRNA response to DON increased over time and declined after removal of contaminated diets. Receiver operating curve analyses for individual microRNAs were significant, and a combination of the four microRNAs increased the predictive capacity for DON exposure. Predicted microRNA target genes showed enrichment of several pathways including PIK3-AKT, Wnt/β-catenin, and adherens junctions. This study gives, for the first time, a comprehensive view of the porcine microRNA response to DON, providing a basis for future research on microRNAs as biomarkers for mycotoxins.

TargetNet: functional microRNA target prediction with deep neural networks.

MicroRNAs (miRNAs) play pivotal roles in gene expression regulation by binding to target sites of messenger RNAs (mRNAs). While identifying functional targets of miRNAs is of utmost importance, their prediction remains a great challenge. Previous computational algorithms have major limitations. They use conservative candidate target site (CTS) selection criteria mainly focusing on canonical site types, rely on laborious and time-consuming manual feature extraction, and do not fully capitalize on the information underlying miRNA-CTS interactions. In this article, we introduce TargetNet, a novel deep learning-based algorithm for functional miRNA target prediction. To address the limitations of previous approaches, TargetNet has three key components: (i) relaxed CTS selection criteria accommodating irregularities in the seed region, (ii) a novel miRNA-CTS sequence encoding scheme incorporating extended seed region alignments and (iii) a deep residual network-based prediction model. The proposed model was trained with miRNA-CTS pair datasets and evaluated with miRNA-mRNA pair datasets. TargetNet advances the previous state-of-the-art algorithms used in functional miRNA target classification. Furthermore, it demonstrates great potential for distinguishing high-functional miRNA targets. The codes and pre-trained models are available at https://github.com/mswzeus/TargetNet.

Bioinformatics Prediction and Analysis of MicroRNAs and Their Targets as Biomarkers for Prostate Cancer: A Preliminary Study.

Prostate cancer (PCa) is the second most common form of cancer in men around the world. Due to its heterogeneity, presentations range from aggressive lethal disease to indolent disease. There is a need to identify core biomarkers that are important for early detection and progression, allowing a more precise method for the treatment and management of Pca. We obtained metastatic prostate cancer associated microRNA array profiles from the GSE28029 dataset in the GEO database. MicroRNA target prediction was done using the databases, TargetScanHuman, miRDB and DIANA microT, six target genes (FOXC1, CDKN1A, BIRC2, CTNND1, ELK1 and LRP8) were found to be common among the three different databases. Differential expression of the target genes was performed via the GENT2 database in the GPL96 platform (HG-U133A). Results indicated all genes were downregulated. Gene Ontology (GO) was used to perform enrichment analysis. The GO enrichment analysis indicated that the downregulated genes were enriched in cellular response to gamma radiation, regulation of transcription and response to drugs as well as protein binding and receptor signaling protein activity. The study suggested that CDKN1A, FOXC1 and BIRC2 might be core genes for prostate cancer that play an important role in its diagnosis, development and progression.

MicroSalmon: A Comprehensive, Searchable Resource of Predicted MicroRNA Targets and 3'UTR Cis-Regulatory Elements in the Full-Length Sequenced Atlantic Salmon Transcriptome.

Complete 3′UTRs unambiguously assigned to specific mRNA isoforms from the Atlantic salmon full-length (FL) transcriptome were collected into a 3′UTRome. miRNA response elements (MREs) and other cis-regulatory motifs were subsequently predicted and assigned to 3′UTRs of all FL-transcripts. The MicroSalmon GitHub repository provides all results. RNAHybrid and sRNAtoolbox tools predicted the MREs. UTRscan and the Teiresias algorithm predicted other 3′UTR cis-acting motifs, both known vertebrate motifs and putative novel motifs. MicroSalmon provides search programs to retrieve all FL-transcripts targeted by a miRNA (median number 1487), all miRNAs targeting an FL-transcript (median number 27), and other cis-acting motifs. As thousands of FL-transcripts may be targets of each miRNA, additional experimental strategies are necessary to reduce the likely true and relevant targets to a number that may be functionally validated. Low-complexity motifs known to affect mRNA decay in vertebrates were over-represented. Many of these were enriched in the terminal end, while purine- or pyrimidine-rich motifs with unknown functions were enriched immediately downstream of the stop codon. Furthermore, several novel complex motifs were over-represented, indicating conservation and putative function. In conclusion, MicroSalmon is an extensive and useful, searchable resource for study of Atlantic salmon transcript regulation by miRNAs and cis-acting 3′UTR motifs.