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Differential microRNA (miRNA or miR) regulation is linked to the development and progress of many diseases, including inflammatory bowel disease (IBD). It is well-established that miRNAs are involved in the differentiation, maturation, and functional control of immune cells. miRNAs modulate inflammatory cascades and affect the extracellular matrix, tight junctions, cellular hemostasis, and microbiota. This review summarizes current knowledge of differentially expressed miRNAs in mucosal tissues and peripheral blood of patients with ulcerative colitis and Crohn's disease. We combined comprehensive literature curation with computational meta-analysis of publicly available high-throughput datasets to obtain a consensus set of miRNAs consistently differentially expressed in mucosal tissues. We further describe the role of the most relevant differentially expressed miRNAs in IBD, extract their potential targets involved in IBD, and highlight their diagnostic and therapeutic potential for future investigations., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Yarani, Shojaeian, Palasca, Doncheva, Jensen, Gorodkin and Pociot.)

The number of discovered natural miRNA sponges in plants, viruses, and mammals is increasing steadily. Some sponges like ciRS-7 for miR-7 contain multiple nearby miRNA binding sites. We hypothesize that such clusters of miRNA binding sites on the genome can function together as a sponge. No systematic effort has been made in search for clusters of miRNA targets. Here, we, to our knowledge, make the first genome-wide target site predictions for clusters of mature human miRNAs. For each miRNA, we predict the target sites on a genome-wide scale, build a graph with edge weights based on the pairwise distances between sites, and apply Markov clustering to identify genomic regions with high binding site density. Significant clusters are then extracted based on cluster size difference between real and shuffled genomes preserving local properties such as the GC content. We then use conservation and binding energy to filter a final set of miRNA target site clusters or sponge candidates. Our pipeline predicts 3673 sponge candidates for 1250 miRNAs, including the experimentally verified miR-7 sponge ciRS-7. In addition, we point explicitly to 19 high-confidence candidates overlapping annotated genomic sequence. The full list of candidates is freely available at http://rth.dk/resources/mirnasponge, where detailed properties for individual candidates can be explored, such as alignment details, conservation, accessibility and target profiles, which facilitates selection of sponge candidates for further context specific analysis., Competing Interests: The authors have declared that no competing interests exist.

The innate immune system is paramount in the response to and clearance of influenza A virus (IAV) infection in non-immune individuals. Known factors include type I and III interferons and antiviral pathogen recognition receptors, and the cascades of antiviral and pro- and anti-inflammatory gene expression they induce. MicroRNAs (miRNAs) are increasingly recognized to participate in post-transcriptional modulation of these responses, but the temporal dynamics of how these players of the antiviral innate immune response collaborate to combat infection remain poorly characterized. We quantified the expression of miRNAs and protein coding genes in the lungs of pigs 1, 3, and 14 days after challenge with swine IAV (H1N2). Through RT-qPCR we observed a 400-fold relative increase in IFN-λ3 gene expression on day 1 after challenge, and a strong interferon-mediated antiviral response was observed on days 1 and 3 accompanied by up-regulation of genes related to the pro-inflammatory response and apoptosis. Using small RNA sequencing and qPCR validation we found 27 miRNAs that were differentially expressed after challenge, with the highest number of regulated miRNAs observed on day 3. In contrast, the number of protein coding genes found to be regulated due to IAV infection peaked on day 1. Pulmonary miRNAs may thus be aimed at fine-tuning the initial rapid inflammatory response after IAV infection. Specifically, we found five miRNAs (ssc-miR-15a, ssc-miR-18a, ssc-miR-21, ssc-miR-29b, and hsa-miR-590-3p)-four known porcine miRNAs and one novel porcine miRNA candidate-to be potential modulators of viral pathogen recognition and apoptosis. A total of 11 miRNAs remained differentially expressed 14 days after challenge, at which point the infection had cleared. In conclusion, the results suggested a role for miRNAs both during acute infection as well as later, with the potential to influence lung homeostasis and susceptibility to secondary infections in the lungs of pigs after IAV infection.

t(8;21) acute myeloid leukemia (AML) is characterized by a translocation between chromosomes 8 and 21 and formation of a distinctive RUNX1-RUNX1T1 fusion transcript. This translocation places RUNX1T1 under control of the RUNX1 promoter leading to a pronounced upregulation of RUNX1T1 transcripts in t(8;21) AML, compared to normal hematopoietic cells. We investigated the role of highly-upregulated RUNX1T1 under the hypothesis that it acts as competing endogenous RNA (ceRNA) titrating microRNAs (miRNAs) away from their target transcripts and thus contributes to AML formation. Using publicly available t(8;21) AML RNA-Seq and miRNA-Seq data available from The Cancer Genome Atlas (TCGA) project, we obtained a network consisting of 605 genes that may act as ceRNAs competing for miRNAs with the suggested RUNX1T1 miRNA sponge. Among the 605 ceRNA candidates, 121 have previously been implied in cancer development. Players in the integrin, cadherin, and Wnt signaling pathways affected by the RUNX1T1 sponge were overrepresented. Finally, among a set of 21 high interest RUNX1T1 ceRNAs we found multiple genes that have previously been linked to AML formation. In conclusion, our study offers a novel look at the role of the RUNX1-RUNX1T1 fusion transcript in t(8;21) AML beyond previously investigated genetic and epigenetic aberrations., (Copyright © 2017 Elsevier B.V. All rights reserved.)

Protein association networks can be inferred from a range of resources including experimental data, literature mining and computational predictions. These types of evidence are emerging for non-coding RNAs (ncRNAs) as well. However, integration of ncRNAs into protein association networks is challenging due to data heterogeneity. Here, we present a database of ncRNA-RNA and ncRNA-protein interactions and its integration with the STRING database of protein-protein interactions. These ncRNA associations cover four organisms and have been established from curated examples, experimental data, interaction predictions and automatic literature mining. RAIN uses an integrative scoring scheme to assign a confidence score to each interaction. We demonstrate that RAIN outperforms the underlying microRNA-target predictions in inferring ncRNA interactions. RAIN can be operated through an easily accessible web interface and all interaction data can be downloaded.Database URL: http://rth.dk/resources/rain., (© The Author(s) 2017. Published by Oxford University Press.)

Obesity and its comorbidities are an increasing challenge for both affected individuals and health care systems, worldwide. In obese individuals, perturbation of expression of both protein-coding genes and microRNAs (miRNA) are seen in obesity-relevant tissues (i.e. adipose tissue, liver and skeletal muscle). miRNAs are small non-coding RNA molecules which have important regulatory roles in a wide range of biological processes, including obesity. Rodents are widely used animal models for human diseases including obesity. However, not all research is applicable for human health or diseases. In contrast, pigs are emerging as an excellent animal model for obesity studies, due to their similarities in their metabolism, their digestive tract and their genetics, when compared to humans. The Göttingen minipig is a small sized easy-to-handle pig breed which has been extensively used for modeling human obesity, due to its capacity to develop severe obesity when fed ad libitum. The aim of this study was to identify differentially expressed of protein-coding genes and miRNAs in a Göttingen minipig obesity model. Liver, skeletal muscle and abdominal adipose tissue were sampled from 7 lean and 7 obese minipigs. Differential gene expression was investigated using high-throughput quantitative real-time PCR (qPCR) on 90 mRNAs and 72 miRNAs. The results revealed de-regulation of several obesity and inflammation-relevant protein-coding genes and miRNAs in all tissues examined. Many genes that are known to be de-regulated in obese humans were confirmed in the obese minipigs and several of these genes have target sites for miRNAs expressed in the opposing direction of the gene, confirming miRNA-mediated regulation in obesity. These results confirm the translational value of the pig for human obesity studies., Competing Interests: The obese minipigs were donated by Novo Nordisk A/S as a part of a scientific collaboration between University of Copenhagen (KU) and Technical University of Denmark (DTU) in 2011. We have reused the animal material from this collaboration. Novo Nordisk A/S was not involved in the present study and the authors have not received any funding or pay from Novo Nordisk A/S. There are no patents, products in development or marketed products to declare. This does not alter our adherence to all the PLOS ONE policies on sharing data and material.

Obesity is a complex condition that increases the risk of life threatening diseases such as cardiovascular disease and diabetes. Studying the gene regulation of obesity is important for understanding the molecular mechanisms behind the obesity derived diseases and may lead to better intervention and treatment plans. MicroRNAs (miRNAs) are short non-coding RNAs regulating target mRNA by binding to their 3'UTR. They are involved in numerous biological processes and diseases, including obesity. In this study we use a mixed breed pig model designed for obesity studies to investigate differentially expressed miRNAs in subcutaneous adipose tissue by RNA sequencing (RNAseq). Both male and female pigs are included to explore gender differences. The RNAseq study shows that the most highly expressed miRNAs are in accordance with comparable studies in pigs and humans. A total of six miRNAs are differentially expressed in subcutaneous adipose tissue between the lean and obese group of pigs, and in addition gender specific significant differential expression is observed for a number of miRNAs. The differentially expressed miRNAs have been verified using qPCR. The results of these studies in general confirm the trends found by RNAseq. Mir-9 and mir-124a are significantly differentially expressed with large fold changes in subcutaneous adipose tissue between lean and obese pigs. Mir-9 is more highly expressed in the obese pigs with a fold change of 10 and a p-value < 0.001. Mir-124a is more highly expressed in the obese pigs with a fold change of 114 and a p-value < 0.001. In addition, mir-124a is significantly higher expressed in abdominal adipose tissue in male pigs with a fold change of 119 and a p-value < 0.05. Both miRNAs are also significantly higher expressed in the liver of obese male pigs where mir-124a has a fold change of 12 and mir-9 has a fold change of 1.6, both with p-values < 0.05.

Post-transcriptional processing events related to short RNAs are often reflected in their read profile patterns emerging from high-throughput sequencing data. MicroRNA arm switching across different tissues is a well-known example of what we define as differential processing. Here, short RNAs from the nine cell lines of the ENCODE project, irrespective of their annotation status, were analyzed for genomic loci representing differential or coherent processing. We observed differential processing predominantly in RNAs annotated as miRNA, snoRNA or tRNA. Four out of five known cases of differentially processed miRNAs that were in the input dataset were recovered and several novel cases were discovered. In contrast to differential processing, coherent processing is observed widespread in both annotated and unannotated regions. While the annotated loci predominantly consist of ~24 nt short RNAs, the unannotated loci comparatively consist of ~17 nt short RNAs. Furthermore, these ~17 nt short RNAs are significantly enriched for overlap to transcription start sites and DNase I hypersensitive sites (p-value < 0.01) that are characteristic features of transcription initiation RNAs. We discuss how the computational pipeline developed in this study has the potential to be applied to other forms of RNA-seq data for further transcriptome-wide studies of differential and coherent processing.

Motivation: MicroRNAs (miRNAs) are a highly abundant class of non-coding RNA genes involved in cellular regulation and thus also diseases. Despite miRNAs being important disease factors, miRNA-disease associations remain low in number and of variable reliability. Furthermore, existing databases and prediction methods do not explicitly facilitate forming hypotheses about the possible molecular causes of the association, thereby making the path to experimental follow-up longer., Results: Here we present miRPD in which miRNA-Protein-Disease associations are explicitly inferred. Besides linking miRNAs to diseases, it directly suggests the underlying proteins involved, which can be used to form hypotheses that can be experimentally tested. The inference of miRNAs and diseases is made by coupling known and predicted miRNA-protein associations with protein-disease associations text mined from the literature. We present scoring schemes that allow us to rank miRNA-disease associations inferred from both curated and predicted miRNA targets by reliability and thereby to create high- and medium-confidence sets of associations. Analyzing these, we find statistically significant enrichment for proteins involved in pathways related to cancer and type I diabetes mellitus, suggesting either a literature bias or a genuine biological trend. We show by example how the associations can be used to extract proteins for disease hypothesis., Availability and Implementation: All datasets, software and a searchable Web site are available at http://mirpd.jensenlab.org.

Traditional mutation assessment methods generally focus on predicting disruptive changes in protein-coding regions rather than non-coding regulatory regions like untranslated regions (UTRs) of mRNAs. The UTRs, however, are known to have many sequence and structural motifs that can regulate translational and transcriptional efficiency and stability of mRNAs through interaction with RNA-binding proteins and other non-coding RNAs like microRNAs (miRNAs). In a recent study, transcriptomes of tumor cells harboring mutant and wild-type KRAS (V-Ki-ras2 Kirsten rat sarcoma viral oncogene homolog) genes in patients with non-small cell lung cancer (NSCLC) have been sequenced to identify single nucleotide variations (SNVs). About 40% of the total SNVs (73,717) identified were mapped to UTRs, but omitted in the previous analysis. To meet this obvious demand for analysis of the UTRs, we designed a comprehensive pipeline to predict the effect of SNVs on two major regulatory elements, secondary structure and miRNA target sites. Out of 29,290 SNVs in 6462 genes, we predict 472 SNVs (in 408 genes) affecting local RNA secondary structure, 490 SNVs (in 447 genes) affecting miRNA target sites and 48 that do both. Together these disruptive SNVs were present in 803 different genes, out of which 188 (23.4%) were previously known to be cancer-associated. Notably, this ratio is significantly higher (one-sided Fisher's exact test p-value = 0.032) than the ratio (20.8%) of known cancer-associated genes (n = 1347) in our initial data set (n = 6462). Network analysis shows that the genes harboring disruptive SNVs were involved in molecular mechanisms of cancer, and the signaling pathways of LPS-stimulated MAPK, IL-6, iNOS, EIF2 and mTOR. In conclusion, we have found hundreds of SNVs which are highly disruptive with respect to changes in the secondary structure and miRNA target sites within UTRs. These changes hold the potential to alter the expression of known cancer genes or genes linked to cancer-associated pathways.

Background: MicroRNAs (miRNAs) are a class of non-protein-coding genes that play a crucial regulatory role in mammalian development and disease. Whereas a large number of miRNAs have been annotated at the structural level during the latest years, functional annotation is sparse. Actinobacillus pleuropneumoniae (APP) causes serious lung infections in pigs. Severe damage to the lungs, in many cases deadly, is caused by toxins released by the bacterium and to some degree by host mediated tissue damage. However, understanding of the role of microRNAs in the course of this infectious disease in porcine is still very limited., Results: In this study, the RNA extracted from visually unaffected and necrotic tissue from pigs infected with Actinobacillus pleuropneumoniae was subjected to small RNA deep sequencing. We identified 169 conserved and 11 candidate novel microRNAs in the pig. Of these, 17 were significantly up-regulated in the necrotic sample and 12 were down-regulated. The expression analysis of a number of candidates revealed microRNAs of potential importance in the innate immune response. MiR-155, a known key player in inflammation, was found expressed in both samples. Moreover, miR-664-5p, miR-451 and miR-15a appear as very promising candidates for microRNAs involved in response to pathogen infection., Conclusions: This is the first study revealing significant differences in composition and expression profiles of miRNAs in lungs infected with a bacterial pathogen. Our results extend annotation of microRNA in pig and provide insight into the role of a number of microRNAs in regulation of bacteria induced immune and inflammatory response in porcine lung.

Background: Transcription factors (TFs) have long been known to be principally activators of transcription in eukaryotes and prokaryotes. The growing awareness of the ubiquity of microRNAs (miRNAs) as suppressive regulators in eukaryotes, suggests the possibility of a mutual, preferential, self-regulatory connectivity between miRNAs and TFs. Here we investigate the connectivity from TFs and miRNAs to other genes and each other using text mining, TF promoter binding site and 6 different miRNA binding site prediction methods., Results: In the first approach text mining of PubMed abstracts reveal statistically significant associations between miRNAs and both TFs and signal transduction gene classes. Secondly, prediction of miRNA targets in human and mouse 3'UTRs show enrichment only for TFs but not consistently across prediction methods for signal transduction or other gene classes. Furthermore, a random sample of 986 TarBase entries was scored for experimental evidence by manual inspection of the original papers, and enrichment for TFs was observed to increase with score. Low-scoring TarBase entries, where experimental evidence is anticorrelated miRNA:mRNA expression with predicted miRNA targets, appear not to select for real miRNA targets to any degree. Our manually validated text-mining results also suggests that miRNAs may be activated by more TFs than other classes of genes, as 7% of miRNA:TF co-occurrences in the literature were TFs activating miRNAs. This was confirmed when thirdly, we found enrichment for predicted, conserved TF binding sites in miRNA and TF genes compared to other gene classes., Conclusions: We see enrichment of connections between miRNAs and TFs using several independent methods, suggestive of a network of mutual activating and suppressive regulation. We have also built regulatory networks (containing 2- and 3-loop motifs) for mouse and human using predicted miRNA and TF binding sites and we have developed a web server to search and display these loops, available for the community at http://rth.dk/resources/tfmirloop.

MicroRNAs (miRNA) are a group of short noncoding RNAs that regulate gene expression at the posttranscriptional level. They are involved in many biological processes, including development, differentiation, apoptosis, and carcinogenesis. Because miRNAs may play a role in the initiation and progression of cancer, they comprise a novel class of promising diagnostic and prognostic molecular markers and potential drug targets. By applying an LNA-enhanced microarray platform, we studied the expression profiles of 955 miRNAs in the NCI-60 cancer cell lines and identified tissue- and cell-type-specific miRNA patterns by unsupervised hierarchical clustering and statistical analysis. A comparison of our data to three previously published miRNA expression studies on the NCI-60 panel showed a remarkably high correlation between the different technical platforms. In addition, the current work contributes expression data for 369 miRNAs that have not previously been profiled. Finally, by matching drug sensitivity data for the NCI-60 cells to their miRNA expression profiles, we found numerous drug-miRNAs pairs, for which the miRNA expression and drug sensitivity profiles were highly correlated and thus represent potential candidates for further investigation of drug resistance and sensitivity mechanisms., (©2011 AACR.)

Unlabelled: MicroRNAs (miRNAs) are a group of small, approximately 21 nt long, riboregulators inhibiting gene expression at a post-transcriptional level. Their most distinctive structural feature is the foldback hairpin of their precursor pre-miRNAs. Even though each pre-miRNA deposited in miRBase has its secondary structure already predicted, little is known about the patterns of structural conservation among pre-miRNAs. We address this issue by clustering the human pre-miRNA sequences based on pairwise, sequence and secondary structure alignment using FOLDALIGN, followed by global multiple alignment of obtained clusters by WAR. As a result, the common secondary structure was successfully determined for four FOLDALIGN clusters: the RF00027 structural family of the Rfam database and three clusters with previously undescribed consensus structures., Availability: http://genome.ku.dk/resources/mirclust

In 2001 there were four PubMed entries matching the word "microRNA" (miRNA). Interestingly, this number has now far exceeded 1300 and is still rapidly increasing. This more than anything demonstrates the extreme attention this field has had within a short period of time. With the large amounts of sequence data being generated, the need for analysis by computational approaches is obvious. Here, we review the general principles used in computational gene and target finding, and discuss the strengths and weaknesses of the methods. Several methods rely on detection of evolutionary conserved candidates, but recent methods have challenged this paradigm by simultaneously searching for the gene and the corresponding target(s). Whereas the early methods made predictions based on sets of hand-derived rules from precursor-miRNA structure or observed target-miRNA interactions, recent methods apply machine learning techniques. Even though these methods are already powerful, the amount of data they rely on is still limited. Since it is evident that data are continuously being generated, it must be anticipated that these methods will further improve their performance.

The processing of micro RNAs (miRNAs) from their stemloop precursor have revealed asymmetry in the processing of the mature and its star sequence. Furthermore, the miRNA processing system between organism differ. To assess this at the sequence level we have investigated mature miRNAs in their genomic contexts. We have compared profiles of mature miRNAs within their genomic context of the 5' and 3' stemloop precursor arms and we find asymmetry between mature sequences of the 5' and 3' stemloop precursor arms. The main observation is that vertebrate organisms have a characteristic motif on the 5' arm which is in contrast to the 3' arm motif which mainly show the conserved U at the position of the mature start. Also the vertebrate 5' arm motif show a semi-conserved G 13 nucleotides upstream from the first position. We compared the 5' and 3' arm profiles using the average log likelihood ratio (ALLR) score, as defined by Wang and Stormo (2003) [Wang T., Stormo, G.D., 2003. Combining phylogenetic data with co-regulated genes to identify regulatory motifs. Bioinformatics 2369-2380.] and computing a p-value we find that the two profiles differs significantly in their 3' end where the 5' arm motif (in contrast to the 3' arm motif) has a semi-conserved GU rich region. Similar findings are also obtained for other organisms, such as fly, worm and plants. The observed similarities and differences between closely and distantly related organisms are discussed and related to current knowledge of miRNA processing.

We have identified the first porcine microRNA (miRNA) cluster (the mir17-92 cluster) and localized it to the q-arm of pig Chromosome 11. The miRNA cluster was found by sequence similarity search with human miRNA sequences against the pig genomic data generated within the Sino-Danish pig genome project. The resulting data contained three complete and two incomplete miRNA precursors of seven miRNAs from the human mir17-92 cluster. Because there is a 100% sequence identity between the four pig miRNAs and the corresponding human miRNAs, the sequences of three unavailable pig miRNAs were derived from the human data. The expression profiles of seven studied miRNAs were analyzed by hybridization to Northern blots containing five porcine tissues: cerebellum, cortex, hippocampus, kidney, and liver. In order to determine the localization of the mir17-92 cluster in the pig genome, we mapped it by PCR in the porcine somatic cell hybrid (SCH) panel and in the INRA-University of Minnesota (INRA-UMN) porcine radiation hybrid (IMpRH) panel. The PCR results enabled us to localize this cluster to the q-arm of pig Chromosome 11 and map it in relation to two microsatellites. Our study presents the first expression analyses of miRNAs in pig and adds information for further functional studies in this species.