Journal articles on the topic 'MiRNA target'
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1
Li, Peng, Yi Chen, Conslata Awino Juma, Chengyong Yang, Jinfeng Huang, Xiaoxiao Zhang, and Yan Zeng. "Differential Inhibition of Target Gene Expression by Human microRNAs." Cells 8, no. 8 (July 30, 2019): 791. http://dx.doi.org/10.3390/cells8080791.
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microRNAs (miRNAs) exert their functions by repressing the expression of their target genes, but most miRNA target genes are unknown, and the degree to which a miRNA differentially inhibits the expression of its targets is underappreciated. We selected human miR-1, miR-122, and miR-124 as representatives to investigate the reliability of miRNA target predictions and examine how miRNAs suppress their targets. We constructed miRNA target gene reporter libraries based on prediction programs TargetScan, miRanda, and PicTar, and performed large-scale reporter assays to directly evaluate whether and how strongly a predicted target gene is repressed by its miRNA. We then performed statistical analyses to examine parameters that contributed to the miRNA inhibition of target genes. We found that the three programs have approximately 72–85% success rates in predicting genuine targets and that the miRNA inhibition of different targets varies in extent. We also identified parameters that could predict the degrees of miRNA repression, and further showed that differential miR-124 repression might contribute to differential gene expression in vivo. Our studies systematically investigated hundreds of miRNA target genes, shed light on factors influencing miRNA functions, and suggested a new mechanism by which differential target repression by miRNAs regulates endogenous gene expression.
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Komatsu, Shintaro, Hiroki Kitai, and Hiroshi I. Suzuki. "Network Regulation of microRNA Biogenesis and Target Interaction." Cells 12, no. 2 (January 13, 2023): 306. http://dx.doi.org/10.3390/cells12020306.
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MicroRNAs (miRNAs) are versatile, post-transcriptional regulators of gene expression. Canonical miRNAs are generated through the two-step DROSHA- and DICER-mediated processing of primary miRNA (pri-miRNA) transcripts with optimal or suboptimal features for DROSHA and DICER cleavage and loading into Argonaute (AGO) proteins, whereas multiple hairpin-structured RNAs are encoded in the genome and could be a source of non-canonical miRNAs. Recent advances in miRNA biogenesis research have revealed details of the structural basis of miRNA processing and cluster assistance mechanisms that facilitate the processing of suboptimal hairpins encoded together with optimal hairpins in polycistronic pri-miRNAs. In addition, a deeper investigation of miRNA–target interaction has provided insights into the complexity of target recognition with distinct outcomes, including target-mediated miRNA degradation (TDMD) and cooperation in target regulation by multiple miRNAs. Therefore, the coordinated or network regulation of both miRNA biogenesis and miRNA–target interaction is prevalent in miRNA biology. Alongside recent advances in the mechanistic investigation of miRNA functions, this review summarizes recent findings regarding the ordered regulation of miRNA biogenesis and miRNA–target interaction.
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Chen, Yuhao, and Xiaowei Wang. "miRDB: an online database for prediction of functional microRNA targets." Nucleic Acids Research 48, no. D1 (August 31, 2019): D127—D131. http://dx.doi.org/10.1093/nar/gkz757.
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Abstract MicroRNAs (miRNAs) are small noncoding RNAs that act as master regulators in many biological processes. miRNAs function mainly by downregulating the expression of their gene targets. Thus, accurate prediction of miRNA targets is critical for characterization of miRNA functions. To this end, we have developed an online database, miRDB, for miRNA target prediction and functional annotations. Recently, we have performed major updates for miRDB. Specifically, by employing an improved algorithm for miRNA target prediction, we now present updated transcriptome-wide target prediction data in miRDB, including 3.5 million predicted targets regulated by 7000 miRNAs in five species. Further, we have implemented the new prediction algorithm into a web server, allowing custom target prediction with user-provided sequences. Another new database feature is the prediction of cell-specific miRNA targets. miRDB now hosts the expression profiles of over 1000 cell lines and presents target prediction data that are tailored for specific cell models. At last, a new web query interface has been added to miRDB for prediction of miRNA functions by integrative analysis of target prediction and Gene Ontology data. All data in miRDB are freely accessible at http://mirdb.org.
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Mohebbi, Mohammad, Liang Ding, Russell L. Malmberg, Cory Momany, Khaled Rasheed, and Liming Cai. "Accurate prediction of human miRNA targets via graph modeling of the miRNA-target duplex." Journal of Bioinformatics and Computational Biology 16, no. 04 (August 2018): 1850013. http://dx.doi.org/10.1142/s0219720018500130.
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miRNAs are involved in many critical cellular activities through binding to their mRNA targets, e.g. in cell proliferation, differentiation, death, growth control, and developmental timing. Accurate prediction of miRNA targets can assist efficient experimental investigations on the functional roles of miRNAs. Their prediction, however, remains a challengeable task due to the lack of experimental data about the tertiary structure of miRNA-target binding duplexes. In particular, correlations of nucleotides in the binding duplexes may not be limited to the canonical Watson Crick base pairs (BPs) as they have been perceived; methods based on secondary structure prediction (typically minimum free energy (MFE)) have only had mix success. In this work, we characterized miRNA binding duplexes with a graph model to capture the correlations between pairs of nucleotides of an miRNA and its target sequences. We developed machine learning algorithms to train the graph model to predict the target sites of miRNAs. In particular, because imbalance between positive and negative samples can significantly deteriorate the performance of machine learning methods, we designed a novel method to re-sample available dataset to produce more informative data learning process. We evaluated our model and miRNA target prediction method on human miRNAs and target data obtained from mirTarBase, a database of experimentally verified miRNA-target interactions. The performance of our method in target prediction achieved a sensitivity of 86% with a false positive rate below 13%. In comparison with the state-of-the-art methods miRanda and RNAhybrid on the test data, our method outperforms both of them by a significant margin. The source codes, test sets and model files all are available at http://rna-informatics.uga.edu/?f=software&p=GraB-miTarget .
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Liu, Chun-Jie, Xin Fu, Mengxuan Xia, Qiong Zhang, Zhifeng Gu, and An-Yuan Guo. "miRNASNP-v3: a comprehensive database for SNPs and disease-related variations in miRNAs and miRNA targets." Nucleic Acids Research 49, no. D1 (September 29, 2020): D1276—D1281. http://dx.doi.org/10.1093/nar/gkaa783.
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Abstract MicroRNAs (miRNAs) related single-nucleotide variations (SNVs), including single-nucleotide polymorphisms (SNPs) and disease-related variations (DRVs) in miRNAs and miRNA-target binding sites, can affect miRNA functions and/or biogenesis, thus to impact on phenotypes. miRNASNP is a widely used database for miRNA-related SNPs and their effects. Here, we updated it to miRNASNP-v3 (http://bioinfo.life.hust.edu.cn/miRNASNP/) with tremendous number of SNVs and new features, especially the DRVs data. We analyzed the effects of 7 161 741 SNPs and 505 417 DRVs on 1897 pre-miRNAs (2630 mature miRNAs) and 3′UTRs of 18 152 genes. miRNASNP-v3 provides a one-stop resource for miRNA-related SNVs research with the following functions: (i) explore associations between miRNA-related SNPs/DRVs and diseases; (ii) browse the effects of SNPs/DRVs on miRNA-target binding; (iii) functional enrichment analysis of miRNA target gain/loss caused by SNPs/DRVs; (iv) investigate correlations between drug sensitivity and miRNA expression; (v) inquire expression profiles of miRNAs and their targets in cancers; (vi) browse the effects of SNPs/DRVs on pre-miRNA secondary structure changes; and (vii) predict the effects of user-defined variations on miRNA-target binding or pre-miRNA secondary structure. miRNASNP-v3 is a valuable and long-term supported resource in functional variation screening and miRNA function studies.
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Praher, Daniela, Bob Zimmermann, Rohit Dnyansagar, David J. Miller, Aurelie Moya, Vengamanaidu Modepalli, Arie Fridrich, et al. "Conservation and turnover of miRNAs and their highly complementary targets in early branching animals." Proceedings of the Royal Society B: Biological Sciences 288, no. 1945 (February 24, 2021): 20203169. http://dx.doi.org/10.1098/rspb.2020.3169.
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MicroRNAs (miRNAs) are crucial post-transcriptional regulators that have been extensively studied in Bilateria, a group comprising the majority of extant animals, where more than 30 conserved miRNA families have been identified. By contrast, bilaterian miRNA targets are largely not conserved. Cnidaria is the sister group to Bilateria and thus provides a unique opportunity for comparative studies. Strikingly, like their plant counterparts, cnidarian miRNAs have been shown to predominantly have highly complementary targets leading to transcript cleavage by Argonaute proteins. Here, we assess the conservation of miRNAs and their targets by small RNA sequencing followed by miRNA target prediction in eight species of Anthozoa (sea anemones and corals), the earliest-branching cnidarian class. We uncover dozens of novel miRNAs but only a few conserved ones. Further, given their high complementarity, we were able to computationally identify miRNA targets in each species. Besides evidence for conservation of specific miRNA target sites, which are maintained between sea anemones and stony corals across 500 Myr of evolution, we also find indications for convergent evolution of target regulation by different miRNAs. Our data indicate that cnidarians have only few conserved miRNAs and corresponding targets, despite their high complementarity, suggesting a high evolutionary turnover.
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ZHENG, YUN, and WEIXIONG ZHANG. "ANIMAL MICRORNA TARGET PREDICTION USING DIVERSE SEQUENCE-SPECIFIC DETERMINANTS." Journal of Bioinformatics and Computational Biology 08, no. 04 (August 2010): 763–88. http://dx.doi.org/10.1142/s0219720010004896.
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Many recent studies have shown that access of animal microRNAs (miRNAs) to their complementary sites in target mRNAs is determined by several sequence-specific determinants beyond the seed regions in the 5′ end of miRNAs. These factors have been related to the repressive power of miRNAs and used in some programs to predict the efficacy of miRNA complementary sites. However, these factors have not been systematically examined regarding their capacities for improving miRNA target prediction. We develop a new miRNA target prediction algorithm, called Hitsensor, by incorporating many sequence-specific features that determine complementarities between miRNAs and their targets, in addition to the canonical seed regions in the 5′ ends of miRNAs. We evaluate the performance of our algorithm on 720 known animal miRNA:target pairs in four species, Homo sapiens, Mus musculus, Drosophila melanogaster and Caenorhabditis elegans. Our experimental results show that Hitsensor outperforms five popular existing algorithms, indicating that our unique scheme for quantifying the determinants of complementary sites is effective in improving the performance of a miRNA target prediction algorithm. We also examine the effectiveness of miRNA-mediated repression for the predicted targets by using a published quantitative protein expression dataset of miR-223 knockout in mouse neutrophils. Hitsensor identifies more targets than the existing algorithms, and the predicted targets of Hitsensor show comparable protein level changes to those of the existing algorithms.
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McGeary, Sean E., Kathy S. Lin, Charlie Y. Shi, Thy M. Pham, Namita Bisaria, Gina M. Kelley, and David P. Bartel. "The biochemical basis of microRNA targeting efficacy." Science 366, no. 6472 (December 5, 2019): eaav1741. http://dx.doi.org/10.1126/science.aav1741.
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MicroRNAs (miRNAs) act within Argonaute proteins to guide repression of messenger RNA targets. Although various approaches have provided insight into target recognition, the sparsity of miRNA-target affinity measurements has limited understanding and prediction of targeting efficacy. Here, we adapted RNA bind-n-seq to enable measurement of relative binding affinities between Argonaute-miRNA complexes and all sequences ≤12 nucleotides in length. This approach revealed noncanonical target sites specific to each miRNA, miRNA-specific differences in canonical target-site affinities, and a 100-fold impact of dinucleotides flanking each site. These data enabled construction of a biochemical model of miRNA-mediated repression, which was extended to all miRNA sequences using a convolutional neural network. This model substantially improved prediction of cellular repression, thereby providing a biochemical basis for quantitatively integrating miRNAs into gene-regulatory networks.
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ALKANLI, Nevra, and Arzu AY. "Kanser Gelişimi ve Progresyonunda miRNA’LAR VE miRNA Gen Varyasyonları." Gevher Nesibe Journal IESDR 6, no. 13 (July 25, 2021): 38–45. http://dx.doi.org/10.46648/gnj.226.
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MicroRNAs (miRNAs) are short non-coding RNA class and perform regulatory functions at the post transcriptional level as tumor suppressors or oncogenes. miRNAs are effective in cell differentiation, cell proliferation and apoptosis regulation in normal development processes. miRNA gene variations associated with gene silencing mechanisms, , pri-miRNA, pre-miRNA, mat-miRNA gene variations, genetic cariations in target sites of miRNAs have been identified. Significant changes may occur in miRNA expression levels as a result of genetic variations defined in miRNA genes. Therefore, it is thought that genetic variations in miRNA genes may be biomarkers that can play an important role in cancer formation, prognosis and progression. MiRNA function disorder due to miRNA-mediated dysregulation in target genes that may occur as a result of miRNA gene variations in the diagnosis and progression of various types of cancer should be evaluated. In addition, determining miRNAs and miRNA gene variations in target genes that affect drug behavior in increasing the effectiveness of drugs is very important in terms of developing new treatment methods and different therapeutic strategies for various cancer types. In this review, it is aimed to examine the potential roles of miRNAs and miRNA gene variations in cancer development, progression and treatment.
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Chen, Jiajia, and Liangzhi Li. "Multiple Regression Analysis Reveals MicroRNA Regulatory Networks in Oryza sativa under Drought Stress." International Journal of Genomics 2018 (October 4, 2018): 1–12. http://dx.doi.org/10.1155/2018/9395261.
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Drought is a major abiotic stress that reduces rice development and yield. miRNAs (microRNAs) are known to mediate posttranscriptional regulation under drought stress. Although the importance of individual miRNAs has been established, the crosstalks between miRNAs and mRNAs remain unearthed. Here we performed microarray analysis of miRNAs and matched mRNA expression profiles of drought-treated rice cultivar Nipponbare. Drought-responsive miRNA-mRNA regulations were identified by a combination of a partial least square (PLS) regression approach and sequence-based target prediction. A drought-induced network with 13 miRNAs and 58 target mRNAs was constructed, and four miRNA coregulatory modules were revealed. Functional analysis suggested that drought-response miRNA targets are enriched in hormone signaling, lipid and carbohydrate metabolism, and antioxidant defense. 13 candidate miRNAs and target genes were validated by RT-qPCR, hierarchical clustering, and ROC analysis. Two target genes (DWARF-3 and P0651G05.2) of miRNA coregulatory modules were further verified by RLM-5′ RACE. Together, our integrative study of miRNA-mRNA interaction provided attractive candidates that will help elucidate the drought-response mechanisms in Oryza sativa.
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Kehl, Tim, Christina Backes, Fabian Kern, Tobias Fehlmann, Nicole Ludwig, Eckart Meese, Hans-Peter Lenhof, and Andreas Keller. "About miRNAs, miRNA seeds, target genes and target pathways." Oncotarget 8, no. 63 (November 9, 2017): 107167–75. http://dx.doi.org/10.18632/oncotarget.22363.
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Slattery, Martha L., Jennifer S. Herrick, John R. Stevens, Roger K. Wolff, and Lila E. Mullany. "An Assessment of Database-Validated microRNA Target Genes in Normal Colonic Mucosa: Implications for Pathway Analysis." Cancer Informatics 16 (January 1, 2017): 117693511771640. http://dx.doi.org/10.1177/1176935117716405.
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Background: Determination of functional pathways regulated by microRNAs (miRNAs), while an essential step in developing therapeutics, is challenging. Some miRNAs have been studied extensively; others have limited information. In this study, we focus on 254 miRNAs previously identified as being associated with colorectal cancer and their database-identified validated target genes. Methods: We use RNA-Seq data to evaluate messenger RNA (mRNA) expression for 157 subjects who also had miRNA expression data. In the replication phase of the study, we replicated associations between 254 miRNAs associated with colorectal cancer and mRNA expression of database-identified target genes in normal colonic mucosa. In the discovery phase of the study, we evaluated expression of 18 miRNAs (those with 20 or fewer database-identified target genes along with miR-21-5p, miR-215-5p, and miR-124-3p which have more than 500 database-identified target genes) with expression of 17 434 mRNAs to identify new targets in colon tissue. Seed region matches between miRNA and newly identified targeted mRNA were used to help determine direct miRNA-mRNA associations. Results: From the replication of the 121 miRNAs that had at least 1 database-identified target gene using mRNA expression methods, 97.9% were expressed in normal colonic mucosa. Of the 8622 target miRNA-mRNA associations identified in the database, 2658 (30.2%) were associated with gene expression in normal colonic mucosa after adjusting for multiple comparisons. Of the 133 miRNAs with database-identified target genes by non-mRNA expression methods, 97.2% were expressed in normal colonic mucosa. After adjustment for multiple comparisons, 2416 miRNA-mRNA associations remained significant (19.8%). Results from the discovery phase based on detailed examination of 18 miRNAs identified more than 80 000 miRNA-mRNA associations that had not previously linked to the miRNA. Of these miRNA-mRNA associations, 15.6% and 14.8% had seed matches for CRCh38 and CRCh37, respectively. Conclusions: Our data suggest that miRNA target gene databases are incomplete; pathways derived from these databases have similar deficiencies. Although we know a lot about several miRNAs, little is known about other miRNAs in terms of their targeted genes. We encourage others to use their data to continue to further identify and validate miRNA-targeted genes.
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Patranabis, Somi, and Suvendra Nath Bhattacharyya. "Phosphorylation of Ago2 and Subsequent Inactivation of let-7a RNP-Specific MicroRNAs Control Differentiation of Mammalian Sympathetic Neurons." Molecular and Cellular Biology 36, no. 8 (February 8, 2016): 1260–71. http://dx.doi.org/10.1128/mcb.00054-16.
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MicroRNAs (miRNAs) are small regulatory RNAs that regulate gene expression posttranscriptionally by base pairing to the target mRNAs in animal cells.KRas, an oncogene known to be repressed by let-7a miRNAs, is expressed and needed for the differentiation of mammalian sympathetic neurons and PC12 cells. We documented a loss of let-7a activity during this differentiation process without any significant change in the cellular level of let-7a miRNA. However, the level of Ago2, an essential component that is associated with miRNAs to form RNP-specific miRNA (miRNP) complexes, shows an increase with neuronal differentiation. In this study, differentiation-induced phosphorylation and the subsequent loss of miRNA from Ago2 were noted, and these accounted for the loss of miRNA activity in differentiating neurons. Neuronal differentiation induces the phosphorylation of mitogen-activated protein kinase p38 and the downstream kinase mitogen- and stress-activated protein kinase 1 (MSK1). This in turn upregulates the phosphorylation of Ago2 and ensures the dissociation of miRNA from Ago2 in neuronal cells. MSK1-mediated miRNP inactivation is a prerequisite for the differentiation of neuronal cells, where let-7a miRNA gets unloaded from Ago2 to ensure the upregulation ofKRas, a target of let-7a. We noted that the inactivation of let-7a is both necessary and sufficient for the differentiation of sympathetic neurons.
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Kern, Fabian, Ernesto Aparicio-Puerta, Yongping Li, Tobias Fehlmann, Tim Kehl, Viktoria Wagner, Kamalika Ray, et al. "miRTargetLink 2.0—interactive miRNA target gene and target pathway networks." Nucleic Acids Research 49, W1 (May 1, 2021): W409—W416. http://dx.doi.org/10.1093/nar/gkab297.
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Abstract Which genes, gene sets or pathways are regulated by certain miRNAs? Which miRNAs regulate a particular target gene or target pathway in a certain physiological context? Answering such common research questions can be time consuming and labor intensive. Especially for researchers without computational experience, the integration of different data sources, selection of the right parameters and concise visualization can be demanding. A comprehensive analysis should be central to present adequate answers to complex biological questions. With miRTargetLink 2.0, we develop an all-in-one solution for human, mouse and rat miRNA networks. Users input in the unidirectional search mode either a single gene, gene set or gene pathway, alternatively a single miRNA, a set of miRNAs or an miRNA pathway. Moreover, genes and miRNAs can jointly be provided to the tool in the bidirectional search mode. For the selected entities, interaction graphs are generated from different data sources and dynamically presented. Connected application programming interfaces (APIs) to the tailored enrichment tools miEAA and GeneTrail facilitate downstream analysis of pathways and context-annotated categories of network nodes. MiRTargetLink 2.0 is freely accessible at https://www.ccb.uni-saarland.de/mirtargetlink2.
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Min, Seonwoo, Byunghan Lee, and Sungroh Yoon. "TargetNet: functional microRNA target prediction with deep neural networks." Bioinformatics 38, no. 3 (October 22, 2021): 671–77. http://dx.doi.org/10.1093/bioinformatics/btab733.
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Abstract Motivation 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. Results 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. Availability and implementation The codes and pre-trained models are available at https://github.com/mswzeus/TargetNet.
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Liang, Guoting, Jing Guo, Shuyong Zhang, and Guangcan Zhang. "Integration of small RNAs, degradome, and transcriptome sequencing in Populus × euramericana “Neva” provides insights into the allelopathic interference of para-hydroxybenzoic acid." Canadian Journal of Forest Research 50, no. 4 (April 2020): 422–37. http://dx.doi.org/10.1139/cjfr-2018-0316.
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Allelopathy is a hot topic of research; however, little is known regarding microRNA (miRNA) expression profiles in plants in response to allelochemicals. In this study, we combined the analyses of the transcriptome, small RNAs (sRNAs), and the degradome to identify key regulatory miRNA-targeted circuits under para-hydroxybenzoic acid (pHBA) stress. A total of 739 and 673 miRNAs were identified in leaves and roots, respectively. Of those, 214 and 148 miRNAs were significantly differentially expressed and identified as pHBA-responsive miRNAs in leaves and roots, respectively. The target genes for the pHBA-responsive miRNAs are involved in signal transduction, response to stress, and secondary metabolite pathways. Furthermore, an integrated analysis of the miRNA–target expression profiles was used to screen the 60 differentially expressed target genes from the 46 differentially expressed miRNAs in the leaves and the 51 differentially expressed target genes from the 36 differentially expressed miRNAs in roots. This integrated analysis revealed 17 and 30 pairs of miRNA targets in the leaves and roots, respectively, which had negatively correlated expression profiles. According to a real-time quantitative polymerase chain reaction (PCR) analysis, 14 miRNA–target pairs also exhibited negative correlations. Moreover, four coexpression regulatory networks were constructed based on the profiles of the differentially expressed miRNA–target pairs. These results suggest that comprehensive analyses of transcriptomes, sRNAs, and the degradome provide a useful platform for investigating the molecular mechanism underlying the pHBA-induced stress response in plants.
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Li, Xingsong, Xiaokang Yu, Yuting He, Yuhuan Meng, Jinsheng Liang, Lizhen Huang, Hongli Du, Xueping Wang, and Wanli Liu. "Integrated Analysis of MicroRNA (miRNA) and mRNA Profiles Reveals Reduced Correlation between MicroRNA and Target Gene in Cancer." BioMed Research International 2018 (December 6, 2018): 1–15. http://dx.doi.org/10.1155/2018/1972606.
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Background. Accumulating evidences demonstrated that microRNA-target gene pairs were closely related to tumorigenesis and development. However, the correlation between miRNA and target gene was insufficiently understood, especially its changes between tumor and normal tissues. Objectives. The aim of this study was to evaluate the changes of correlation of miRNAs-target pairs between normal and tumor. Materials and Methods. 5680 mRNA and 5740 miRNA expression profiles of 11 major human cancers were downloaded from the Cancer Genome Atlas (TCGA). The 11 cancer types were bladder urothelial carcinoma, breast invasive carcinoma, head and neck squamous cell carcinoma, kidney chromophobe, kidney renal clear cell carcinoma, kidney renal papillary cell carcinoma, liver hepatocellular carcinoma, lung adenocarcinoma, lung squamous cell carcinoma, stomach adenocarcinoma, and thyroid carcinoma. For each cancer type, we firstly obtained differentially expressed miRNAs (DEMs) and genes (DEGs) in tumor and then acquired critical miRNA-target gene pairs by combining DEMs, DEGs and two experimentally validated miRNA-target interaction databases, miRTarBase and miRecords. We collected samples with both miRNA and mRNA expression values and performed a correlation analysis by Pearson method for miRNA-target pairs in normal and tumor, respectively. Results. We totally got 4743 critical miRNA-target pairs across 11 cancer types, and 4572 of them showed weaker correlation in tumor than in normal. The average correlation coefficients of miRNA-target pairs were different greatly between normal (-0.38 ~ -0.61) and tumor (-0.04 ~ -0.26) for 11 cancer type. The pan-cancer network, which consisted of 108 edges connecting 35 miRNAs and 89 target genes, showed the interactions of pairs appeared in multicancers. Conclusions. This comprehensive analysis revealed that correlation between miRNAs and target genes was greatly reduced in tumor and these critical pairs we got were involved in cellular adhesion, proliferation, and migration. Our research could provide opportunities for investigating cancer molecular regulatory mechanism and seeking therapeutic targets.
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Pérez-Cremades, Daniel, Ana B. Paes, Xavier Vidal-Gómez, Ana Mompeón, Carlos Hermenegildo, and Susana Novella. "Regulatory Network Analysis in Estradiol-Treated Human Endothelial Cells." International Journal of Molecular Sciences 22, no. 15 (July 30, 2021): 8193. http://dx.doi.org/10.3390/ijms22158193.
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Background/Aims: Estrogen has been reported to have beneficial effects on vascular biology through direct actions on endothelium. Together with transcription factors, miRNAs are the major drivers of gene expression and signaling networks. The objective of this study was to identify a comprehensive regulatory network (miRNA–transcription factor–downstream genes) that controls the transcriptomic changes observed in endothelial cells exposed to estradiol. Methods: miRNA/mRNA interactions were assembled using our previous microarray data of human umbilical vein endothelial cells (HUVEC) treated with 17β-estradiol (E2) (1 nmol/L, 24 h). miRNA–mRNA pairings and their associated canonical pathways were determined using Ingenuity Pathway Analysis software. Transcription factors were identified among the miRNA-regulated genes. Transcription factor downstream target genes were predicted by consensus transcription factor binding sites in the promoter region of E2-regulated genes by using JASPAR and TRANSFAC tools in Enrichr software. Results: miRNA–target pairings were filtered by using differentially expressed miRNAs and mRNAs characterized by a regulatory relationship according to miRNA target prediction databases. The analysis identified 588 miRNA–target interactions between 102 miRNAs and 588 targets. Specifically, 63 upregulated miRNAs interacted with 295 downregulated targets, while 39 downregulated miRNAs were paired with 293 upregulated mRNA targets. Functional characterization of miRNA/mRNA association analysis highlighted hypoxia signaling, integrin, ephrin receptor signaling and regulation of actin-based motility by Rho among the canonical pathways regulated by E2 in HUVEC. Transcription factors and downstream genes analysis revealed eight networks, including those mediated by JUN and REPIN1, which are associated with cadherin binding and cell adhesion molecule binding pathways. Conclusion: This study identifies regulatory networks obtained by integrative microarray analysis and provides additional insights into the way estradiol could regulate endothelial function in human endothelial cells.
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Wang, Zixing, Wenlong Xu, Haifeng Zhu, and Yin Liu. "A Bayesian Framework to Improve MicroRNA Target Prediction by Incorporating External Information." Cancer Informatics 13s7 (January 2014): CIN.S16348. http://dx.doi.org/10.4137/cin.s16348.
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MicroRNAs (miRNAs) are small regulatory RNAs that play key gene-regulatory roles in diverse biological processes, particularly in cancer development. Therefore, inferring miRNA targets is an essential step to fully understanding the functional properties of miRNA actions in regulating tumorigenesis. Bayesian linear regression modeling has been proposed for identifying the interactions between miRNAs and mRNAs on the basis of the integrated sequence information and matched miRNA and mRNA expression data; however, this approach does not use the full spectrum of available features of putative miRNA targets. In this study, we integrated four important sequence and structural features of miRNA targeting with paired miRNA and mRNA expression data to improve miRNA-target prediction in a Bayesian framework. We have applied this approach to a gene-expression study of liver cancer patients and examined the posterior probability of each miRNA-mRNA interaction being functional in the development of liver cancer. Our method achieved better performance, in terms of the number of true targets identified, than did other methods.
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Talukder, Amlan, Xiaoman Li, and Haiyan Hu. "Position-wise binding preference is important for miRNA target site prediction." Bioinformatics 36, no. 12 (March 18, 2020): 3680–86. http://dx.doi.org/10.1093/bioinformatics/btaa195.
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Abstract Motivation It is a fundamental task to identify microRNAs (miRNAs) targets and accurately locate their target sites. Genome-scale experiments for miRNA target site detection are still costly. The prediction accuracies of existing computational algorithms and tools are often not up to the expectation due to a large number of false positives. One major obstacle to achieve a higher accuracy is the lack of knowledge of the target binding features of miRNAs. The published high-throughput experimental data provide an opportunity to analyze position-wise preference of miRNAs in terms of target binding, which can be an important feature in miRNA target prediction algorithms. Results We developed a Markov model to characterize position-wise pairing patterns of miRNA–target interactions. We further integrated this model as a scoring method and developed a dynamic programming (DP) algorithm, MDPS (Markov model-scored Dynamic Programming algorithm for miRNA target site Selection) that can screen putative target sites of miRNA-target binding. The MDPS algorithm thus can take into account both the dependency of neighboring pairing positions and the global pairing information. Based on the trained Markov models from both miRNA-specific and general datasets, we discovered that the position-wise binding information specific to a given miRNA would benefit its target prediction. We also found that miRNAs maintain region-wise similarity in their target binding patterns. Combining MDPS with existing methods significantly improves their precision while only slightly reduces their recall. Therefore, position-wise pairing patterns have the promise to improve target prediction if incorporated into existing software tools. Availability and implementation The source code and tool to calculate MDPS score is available at http://hulab.ucf.edu/research/projects/MDPS/index.html. Supplementary information Supplementary data are available at Bioinformatics online.
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Gay, Lauren, Peter Turner, and Rolf Renne. "Contemporary Ribonomics Methods for Viral microRNA Target Analysis." Non-Coding RNA 4, no. 4 (November 9, 2018): 31. http://dx.doi.org/10.3390/ncrna4040031.
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Numerous cellular processes are regulated by microRNAs (miRNAs), both cellular and viral. Elucidating the targets of miRNAs has become an active area of research. An important method in this field is cross-linking and immunoprecipitation (CLIP), where cultured cells or tissues are UV-irradiated to cross-link protein and nucleic acid, the RNA binding protein of interest is immunoprecipitated, and the RNAs pulled down with the protein are isolated, reverse-transcribed, and analyzed by sequencing. CLIP using antibody against Argonaute (Ago), which binds to both miRNA and mRNA as they interact in RISC, has allowed researchers to uncover a large number of miRNA targets. Coupled with high-throughput sequencing, CLIP has been useful for revealing miRNA targetomes for the γ-herpesviruses Kaposi’s sarcoma-associated herpesvirus (KSHV) and Epstein-Barr virus (EBV). Variants on the CLIP protocol are described, with the benefits and drawbacks of each. In particular, the most recent methods involving RNA–RNA ligation to join the miRNA and its RNA target have aided in target identification. Lastly, data supporting biologically meaningful interactions between miRNAs and long non-coding RNAs (lncRNAs) are reviewed. In summary, ribonomics-based miRNA targetome analysis has expanded our understanding of miRNA targeting and has provided a rich resource for EBV and KSHV research with respect to pathogenesis and tumorigenesis.
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Taguchi, Y.-h. "Correlation between miRNA-targeting-specific promotermethylation and miRNA regulation of target genes." F1000Research 2 (January 23, 2013): 21. http://dx.doi.org/10.12688/f1000research.2-21.v1.
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Background miRNA regulation of target genes and promoter methylation were known to be the primary mechanisms underlying the epigenetic regulation of gene expression. However, how these two processes cooperatively regulate gene expression has not been extensively studied. Methods Gene expression and promoter methylation profiles of 271 distinct human cell lines were obtained from gene expression omnibus. P-values that describe both miRNA-targeting-specific promoter methylation and miRNA regulation of target genes were computed with the MiRaGE method proposed recently by the author. Results We found that promoter methylation was miRNA-targeting-specific. In other words, changes in promoter methylation were associated with miRNA binding at target genes. It was also found that miRNA-targeting-specific promoter hypomethylation was related to miRNA regulation; the genes with miRNA-targeting-specific promoter hypomethylation were downregulated during cell senescence and upregulated during cellulardierentiation. Promoter hypomethylation was especially enhanced for genes targeted by miR-548 miRNAs, which are non-conserved, and primate-specific miRNAs that are typically expressed at lower levels than the frequently investigated conserved miRNAs. Conclusions It was found that promoter methylation was affected by miRNA targeting. Furthermore, miRNA-targeting-specific promoter hypomethylation was suggested to facilitate gene regulation by miRNAs that are not strongly expressed (e.g., miR-548 miRNAs).
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Dai, Wennan, Xin Su, Bin Zhang, Kejing Wu, Pengshan Zhao, and Zheng Yan. "An Alternative Class of Targets for microRNAs Containing CG Dinucleotide." Biology 11, no. 3 (March 21, 2022): 478. http://dx.doi.org/10.3390/biology11030478.
Full textAbstract:
MicroRNAs (miRNAs) are endogenous ~23 nt RNAs which regulate message RNA (mRNA) targets mainly through perfect pairing with their seed region (positions 2–7). Several instances of UTR sequence with an additional nucleotide that might form a bulge within the pairing region, can also be recognized by miRNA as their target (bugle-target). But the prevalence of such imperfect base pairings in human and their roles in the evolution are incompletely understood. We found that human miRNAs with the CG dinucleotides (CG dimer) in their seed region have a significant low mutation rate than their putative binding sites in mRNA targets. Interspecific comparation shows that these miRNAs had very few conservative targets with the perfect seed-pairing, while potentially having a subclass of bulge-targets. Compared with the canonical target (perfect seed-pairing), these bulge-targets had a lower negative correlation with the miRNA expression, and either were down-regulated in the miRNA overexpression experiment or up-regulated in the miRNA knock-down experiment. Our results show that the bulge-targets are widespread in the miRNAs with CG dinucleotide within their seed regions, which could in part explain the rare conserved targets of these miRNAs based on seed rule. Incorporating these bulge-targets, together with conservation information, could more accurately predict the entire targets of these miRNAs.
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Bravo-Egana, Valia, Samuel Rosero, Dagmar Klein, Zhijie Jiang, Nancy Vargas, Nicholas Tsinoremas, Marco Doni, et al. "Inflammation-Mediated Regulation of MicroRNA Expression in Transplanted Pancreatic Islets." Journal of Transplantation 2012 (2012): 1–15. http://dx.doi.org/10.1155/2012/723614.
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Nonspecific inflammation in the transplant microenvironment results inβ-cell dysfunction and death influencing negatively graft outcome. MicroRNA (miRNA) expression and gene target regulation in transplanted islets are not yet well characterized. We evaluated the impact of inflammation on miRNA expression in transplanted rat islets. Islets exposedin vitroto proinflammatory cytokines and explanted syngeneic islet grafts were evaluated by miRNA arrays. A subset of 26 islet miRNAs was affected by inflammation bothin vivoandin vitro. Induction of miRNAs was dependent on NF-κB, a pathway linked with cytokine-mediated islet cell death. RT-PCR confirmed expression of 8 miRNAs. The association between these miRNAs and mRNA target-predicting algorithms in genome-wide RNA studies ofβ-cell inflammation identified 238 potential miRNA gene targets. Several genes were ontologically associated with regulation of insulin signaling and secretion, diabetes, and islet physiology. One of the most activated miRNAs was miR-21. Overexpression of miR-21 in insulin-secreting MIN6 cells downregulated endogenous expression of the tumor suppressor Pdcd4 and of Pclo, a Ca2+sensor protein involved in insulin secretion. Bioinformatics identified both as potential targets. The integrated analysis of miRNA and mRNA expression profiles revealed potential targets that may identify molecular targets for therapeutic interventions.
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Riolo, Giulia, Silvia Cantara, Carlotta Marzocchi, and Claudia Ricci. "miRNA Targets: From Prediction Tools to Experimental Validation." Methods and Protocols 4, no. 1 (December 24, 2020): 1. http://dx.doi.org/10.3390/mps4010001.
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MicroRNAs (miRNAs) are post-transcriptional regulators of gene expression in both animals and plants. By pairing to microRNA responsive elements (mREs) on target mRNAs, miRNAs play gene-regulatory roles, producing remarkable changes in several physiological and pathological processes. Thus, the identification of miRNA-mRNA target interactions is fundamental for discovering the regulatory network governed by miRNAs. The best way to achieve this goal is usually by computational prediction followed by experimental validation of these miRNA-mRNA interactions. This review summarizes the key strategies for miRNA target identification. Several tools for computational analysis exist, each with different approaches to predict miRNA targets, and their number is constantly increasing. The major algorithms available for this aim, including Machine Learning methods, are discussed, to provide practical tips for familiarizing with their assumptions and understanding how to interpret the results. Then, all the experimental procedures for verifying the authenticity of the identified miRNA-mRNA target pairs are described, including High-Throughput technologies, in order to find the best approach for miRNA validation. For each strategy, strengths and weaknesses are discussed, to enable users to evaluate and select the right approach for their interests.
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Pallasch, Christian P., Susanne Hagist, Michaela Patz, Alexandra Schulz, Svenja Debey, Daniela Eggle, Joachim L. Schultze, Michael Hallek, and Clemens-Martin Wendtner. "Deregulation of Micrornas Results in Overexpression of Oncogenic Transcription Factors Involved in Pathogenesis of CLL." Blood 112, no. 11 (November 16, 2008): 2075. http://dx.doi.org/10.1182/blood.v112.11.2075.2075.
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Abstract MicroRNAs play a key role in the control of translation and mRNA degradation by binding to the 3′ untranslated regions of mRNAs. Immune cell development is especially dependent on miRNA regulation like miR-155 in germinal center development. Distinct miRNA-deregulation has been recently identified in CLL, however the proposed model of miR-15/16 mediated pathogenesis remains controversial. CLL cells of 50 treatment-naïve patients and peripheral B-cells of 14 healthy donors were separated by untouched depletion and processed for isolation of at least 200 ng total RNA. A new Illumina based Bead Chip was applied using 100 ng of total RNA for reliable hybridization. Target prediction of deregulated miRNAs was performed by in-silico predictions of miRNA-target gene interactions by TargetScan and PicTar. MiRNA target candidates were analyzed for differential protein expression in healthy donor B cells versus CLL cells. The applied technology was repetitively controlled and demonstrated to reveal reliable quantitative results. Comparing CLL samples with healthy donor B cells an overall decrease of miRNAs was observed in CLL samples. In total 19 miRNA were identified to be significantly lower expressed in CLL. 7 miRNAs were overexpressed in CLL. Comparing previously published data we could reliably identify upregulation of miR- 155 and downreguation of miR-181. However, the intensively discussed deregulation of miR-15 and miR-16 could not be verified. Furthermore we identified a so far not described CLL-specific miRNA-fingerprint of 26 miRNAs. Based on this fingerprint we analyzed for miRNA targets based on target prediction. A significantly focused number of transcription factors were identified by this primary screen. We could confirm predicted over-expression of oncogenic transcription factors by immunoblotting analysis. Targeting of deregulated miRNAs to 3′UTR of target genes was assessed by luciferase reporter assays. Decreased activity of 3’UTR-reporter construct was achieved by cotransfection with synthetic miRNAs together with target 3′UTRs. Targeted mutation of putative binding sites revealed an abrogation of miRNA-mediated suppression of luciferase activity confirming specificity of miRNA-3′UTR interaction of target genes. Here we could identify significant down-regulation of miRNA in CLL, a so far not identified cluster of deregulated miRNAs led to the identification of oncogenic transcription factors as novel target genes and pathogenic pathways in CLL. A key question remains regarding the cause of down-regulation of miRNAs mainly observed in CLL. Since no genomic hotspot is apparent, an impaired processing of pre-/pri-miRNAs or suppressive transcription factor loops have to be hypothesized and investigated to reveal the regulatory role of miRNA in malignant transformation of CLL.
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Li, Yameng, Yukun Xu, Yawei Hou, and Rui Li. "Construction and Bioinformatics Analysis of the miRNA-mRNA Regulatory Network in Diabetic Nephropathy." Journal of Healthcare Engineering 2021 (November 18, 2021): 1–11. http://dx.doi.org/10.1155/2021/8161701.
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Background. MicroRNA (miRNA) has been confirmed to be involved in the occurrence, development, and prevention of diabetic nephropathy (DN), but its mechanism of action is still unclear. Objective. With the help of the GEO database, bioinformatics methods are used to explore the miRNA-mRNA regulatory relationship pairs related to diabetic nephropathy and explain their potential mechanisms of action. Methods. The DN-related miRNA microarray dataset (GSE51674) and mRNA expression dataset (GSE30122) are downloaded through the GEO database, online analysis tool GEO2R is used for data differential expression analysis, TargetScan, miRTarBase, and miRDB databases are used to predict potential downstream target genes regulated by differentially expressed miRNAs, and intersection with differential genes is used to obtain candidate target genes. According to the regulatory relationship between miRNA and mRNA, the miRNA-mRNA relationship pair is clarified, and the miRNA-mRNA regulatory network is constructed using Cytoscape. DAVID is used to perform GO function enrichment analysis and KEGG pathway analysis of candidate target genes. By GeneMANIA prediction of miRNA target genes and coexpressed genes, the protein interaction network is constructed. Results and Conclusions. A total of 67 differentially expressed miRNAs were screened in the experiment, of which 42 were upregulated and 25 were downregulated; a total of 448 differentially expressed mRNAs were screened, of which 93 were upregulated and 355 were downregulated. Using TargetScan, miRTarBase, and miRDB databases to predict downstream targets of differentially expressed miRNAs, 2283 downstream target genes coexisting in 3 databases were predicted to intersect with differentially expressed mRNAs to obtain 96 candidate target genes. Finally, 44 miRNA-mRNA relationship pairs consisting of 12 differentially expressed miRNAs and 27 differentially expressed mRNAs were screened out; further analysis showed that miRNA regulatory network genes may participate in the occurrence and development of diabetic nephropathy through PI3K/Akt, ECM-receptor interaction pathway, and RAS signaling pathway.
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Thomas, John P., Marton Ölbei, Johanne Brooks-Warburton, Tamas Korcsmaros, and Dezso Modos. "Analysing miRNA-Target Gene Networks in Inflammatory Bowel Disease and Other Complex Diseases Using Transcriptomic Data." Genes 13, no. 2 (February 18, 2022): 370. http://dx.doi.org/10.3390/genes13020370.
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Patients with inflammatory bowel disease (IBD) are known to have perturbations in microRNA (miRNA) levels as well as altered miRNA regulation. Although experimental methods have provided initial insights into the functional consequences that may arise due to these changes, researchers are increasingly utilising novel bioinformatics approaches to further dissect the role of miRNAs in IBD. The recent exponential increase in transcriptomics datasets provides an excellent opportunity to further explore the role of miRNAs in IBD pathogenesis. To effectively understand miRNA-target gene interactions from gene expression data, multiple database resources are required, which have become available in recent years. In this technical note, we provide a step-by-step protocol for utilising these state-of-the-art resources, as well as systems biology approaches to understand the role of miRNAs in complex disease pathogenesis. We demonstrate through a case study example how to combine the resulting miRNA-target gene networks with transcriptomics data to find potential disease-specific miRNA regulators and miRNA-target genes in Crohn’s disease. This approach could help to identify miRNAs that may have important disease-modifying effects in IBD and other complex disorders, and facilitate the discovery of novel therapeutic targets.
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Kassambara, Alboukadel, Angelique Bruyer, Michel Jourdan, Nicolas Robert, Véronique Pantesco, Olivier Elemento, Bernard Klein, and Jerome Moreaux. "Patterns of Microrna in Plasma Cells: From Normal Differentiation to Multiple Myeloma." Blood 128, no. 22 (December 2, 2016): 2069. http://dx.doi.org/10.1182/blood.v128.22.2069.2069.
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Abstract Background: MicroRNAs (miRNAs) are small noncoding RNAs that regulate gene expression either via the degradation of target mRNAs or the inhibition of protein translation. miRNAs have been involved in fine-tuning critical cellular processes and play critical roles in cell differentiation and tumor development. Furthermore, there is an increasing recognition of the role of miRNAs in multiple myeloma, a plasma cell (PC) malignancy characterized by an accumulation of malignant PCs within the bone marrow. However, little is known about miRNA expression during human PCD as well as about the full extent to which individual miRNAs regulate fundamental processes during PCD. A complete delineation of miRNA and their target expression during normal PCD is essential to understand the role of miRNAs in plasma cell malignancies. Methods: We analyzed the expression profile of miRNAs and mRNAs during human plasma cell differentiation (PCD) to infer miRNA-target relationships, as well as in multiple myeloma tumor plasma cells. We developed a method and an R package, that uses miRNA and mRNA expression profiles across PCD cell subpopulations to infer candidate miRNA-target interactions that could be active and functional in PCD. We inferred miRNA-target relationships from sequence-based prediction, experimentally validated target interactions curated from the literature, published data from miRNA perturbation experiments and inverse expression relationships between miRNAs and their target mRNAs. Results: Our results reveal 63 miRNAs with significant temporal changes in their expression during normal PCD. We derived a high-confidence network of 295 target relationships comprising 47 miRNAs and 147 targets. These relationships include new examples of miRNAs that are likely to coordinately regulate multiple members of critical pathways associated with PCD. Notably, we identify new miRNAs that coordinately regulate important pathways in PCD, including members of miR-30 and miR-29 families, miR-106b and miR-16, which regulates IRF4/PRDM1 axis, active DNA methylation pathway, TGF-b signaling pathway, autophagy, ZBTB4/EZH2 axis and cell cycle progression. Furthermore, our work demonstrates that 28 PCD stage-specific miRNAs are aberrantly overexpressed in multiple myeloma cells (MMCs) compared to their normal counterpart and/or are associated with high risk myeloma, suggesting that MMCs frequently acquired expression changes in miRNAs already undergoing dynamic expression modulation during normal PCD. Finally, expression of some targets of these PCD - MM miRNAs is correlated with clinical outcome of uniformly treated MM patients. Conclusions: Altogether, our results demonstrate that miRNAs may be important in controlling PCD and malignant plasma cell biology. Disclosures No relevant conflicts of interest to declare.
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Kozar, Ines, Demetra Philippidou, Christiane Margue, Lauren A. Gay, Rolf Renne, and Stephanie Kreis. "Cross-Linking Ligation and Sequencing of Hybrids (qCLASH) Reveals an Unpredicted miRNA Targetome in Melanoma Cells." Cancers 13, no. 5 (March 4, 2021): 1096. http://dx.doi.org/10.3390/cancers13051096.
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MicroRNAs are key post-transcriptional gene regulators often displaying aberrant expression patterns in cancer. As microRNAs are promising disease-associated biomarkers and modulators of responsiveness to anti-cancer therapies, a solid understanding of their targetome is crucial. Despite enormous research efforts, the success rates of available tools to reliably predict microRNAs (miRNA)-target interactions remains limited. To investigate the disease-associated miRNA targetome, we have applied modified cross-linking ligation and sequencing of hybrids (qCLASH) to BRAF-mutant melanoma cells. The resulting RNA-RNA hybrid molecules provide a comprehensive and unbiased snapshot of direct miRNA-target interactions. The regulatory effects on selected miRNA target genes in predicted vs. non-predicted binding regions was validated by miRNA mimic experiments. Most miRNA–target interactions deviate from the central dogma of miRNA targeting up to 60% interactions occur via non-canonical seed pairing with a strong contribution of the 3′ miRNA sequence, and over 50% display a clear bias towards the coding sequence of mRNAs. miRNAs targeting the coding sequence can directly reduce gene expression (miR-34a/CD68), while the majority of non-canonical miRNA interactions appear to have roles beyond target gene suppression (miR-100/AXL). Additionally, non-mRNA targets of miRNAs (lncRNAs) whose interactions mainly occur via non-canonical binding were identified in melanoma. This first application of CLASH sequencing to cancer cells identified over 8 K distinct miRNA–target interactions in melanoma cells. Our data highlight the importance non-canonical interactions, revealing further layers of complexity of post-transcriptional gene regulation in melanoma, thus expanding the pool of miRNA–target interactions, which have so far been omitted in the cancer field.
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Taguchi, Y.-h. "Correlation between miRNA-targeted-gene promoter methylation and miRNA regulation of target genes." F1000Research 2 (September 11, 2013): 21. http://dx.doi.org/10.12688/f1000research.2-21.v3.
Full textAbstract:
Background miRNA regulation of target genes and promoter methylation are known to be the primary mechanisms underlying the epigenetic regulation of gene expression. However, how these two processes cooperatively regulate gene expression has not been extensively studied.Methods Gene expression and promoter methylation profiles of 270 distinct human cell lines were obtained from Gene Expression Omnibus. P-values that describe both miRNA-targeted-gene promoter methylation and miRNA regulation of target genes were computed using the MiRaGE method proposed recently by the author.Results Significant changes in promoter methylation were associated with miRNA targeting. It was also found that miRNA-targeted-gene promoter hypomethylation was related to differential target gene expression; the genes with miRNA-targeted-gene promoter hypomethylation were downregulated during cell senescence and upregulated during cellular differentiation. Promoter hypomethylation was especially enhanced for genes targeted by miR-548 miRNAs, which are non-conserved, primate-specific miRNAs that are typically expressed at lower levels than the frequently investigated conserved miRNAs. miRNA-targeted-gene promoter methylation may also be related to the seed region features of miRNA.Conclusions It was found that promoter methylation was correlated to miRNA targeting. Furthermore, miRNA-targeted-gene promoter hypomethylation was especially enhanced in promoters of genes targeted by miRNAs that are not strongly expressed (e.g., miR-548 miRNAs) and was suggested to be highly related to some seed region features of miRNAs.
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Santosh P., Shinde, Neelima Arora, Pranjal Sarma, Manika Pal-Bhadra, and Utpal Bhadra. "Interaction Map and Selection of microRNA Targets in Parkinson's Disease-Related Genes." Journal of Biomedicine and Biotechnology 2009 (2009): 1–11. http://dx.doi.org/10.1155/2009/363145.
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Parkinson's disease (PD) is a complex multigenic neurodisorder frequently occurring in elderly persons. To investigate noncoding tiny microRNA mediated gene regulation, miRanda (version 1.0b) was used to predict human miRNA target sites on selected 29 genes related to PD. To verify output generated from miRanda, a similar analysis was performed only for microRNA target sites in3′UTR using TargetScan (version 5.1). Data extracted by miRanda elucidates the mode of microRNA action based on the location of target sites in the Parkinson genes. Sites prone to action of multiple miRNAs were identified as “hot spots.” Important properties of each miRNA including multiplicity and cooperativity appear to contribute towards a complex interplay between miRNAs and their targets. Two sets of predicted results were explored for the occurrence of target sites of 112 miRNAs expressed in midbrain. Overall, convergence of results predicted by two algorithms revealed that 48 target sites for midbrain-specific miRNA occur in close proximity in 9 genes. This study will pave a way for selection of potential miRNA candidates for Parkinson's disease-related genes for quick therapeutic applications and diagnosis.
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Briskin, Daniel, Peter Y. Wang, and David P. Bartel. "The biochemical basis for the cooperative action of microRNAs." Proceedings of the National Academy of Sciences 117, no. 30 (July 13, 2020): 17764–74. http://dx.doi.org/10.1073/pnas.1920404117.
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In cells, closely spaced microRNA (miRNA) target sites within a messenger RNA (mRNA) can act cooperatively, leading to more repression of the target mRNA than expected by independent action at each site. Using purified miRNA-Argonaute (AGO2) complexes, synthetic target RNAs, and a purified domain of TNRC6B (GW182 in flies) that is able to simultaneously bind multiple AGO proteins, we examined both the occupancies and binding affinities of miRNA-AGO2 complexes and target RNAs with either one site or two cooperatively spaced sites. On their own, miRNA-AGO2 complexes displayed little if any cooperative binding to dual sites. In contrast, in the presence of the AGO-binding region of TNRC6B, we observed strong cooperative binding to dual sites, with almost no singly bound target RNAs and substantially increased binding affinities and Hill coefficients. Cooperative binding was retained when the two sites were for two different miRNAs or when the two sites were bound to miRNAs loaded into two different AGO paralogs, AGO1 and AGO2. The improved binding affinity was attributable primarily to a reduced rate of dissociation between miRNA-AGO complexes and their dual-site targets. Thus, the multivalent binding of TNRC6 enables cooperative binding of miRNA-AGO complexes to target RNAs, thereby explaining the basis of cooperative action.
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Ashizawa, Mai, Hirokazu Okayama, Teruhide Ishigame, Kousaku Mimura, and Koji Kono. "Identification of microRNAs that target PD-L1 in mismatch repair-deficient colorectal cancer." Journal of Clinical Oncology 36, no. 5_suppl (February 10, 2018): 85. http://dx.doi.org/10.1200/jco.2018.36.5_suppl.85.
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85 Background: Programmed cell death 1 (PD-1) / PD-ligand 1 (PD-L1) immune checkpoint blockade has emerged as a promising therapeutic strategy in various types of cancer. Here we report that certain microRNAs (miRNAs) are involved in immunosuppressive microenvironment by directly targeting PD-L1 in mismatch repair deficient (dMMR) colorectal cancer (CRC). Methods: We identified candidate miRNAs by using RNA-sequence analyses for mRNA and miRNA expression obtained from The Cancer Genome Atlas (TCGA) Colon Adenocarcinoma combined with miRNA target prediction programs. HCT116 and SW837 CRC cell lines were transfected with miRNA mimics and inhibitors, and PD-L1 expression was examined by qRT-PCR, western blotting and flow cytometry. The CRC cell lines were co-cultured with activated T cells and then cytotoxicity for T cells were evaluated. Results: Using miRNA expression profiles of 260 tumors obtained through TCGA Colon Adenocarcinoma, 47 miRNA probes were found to be inversely correlated with PD-L1 expression. Among them, 19 mature miRNAs were down-regulated in dMMR tumors. Furthermore, eight in silico miRNA-target prediction programs were utilized to identify candidate miRNAs that target the 3’UTR of PD-L1 mRNA. We found that forced miRNA expression decreased PD-L1 expression in protein (total and surface) and mRNA levels, and they also dramatically decreased IFN-induced cell surface PD-L1 expression by 32% in CRC cell lines. Furthermore, we found that forced miRNA expression decreased PD-L1 associated apoptotic cell death in co-cultured activated T cells. Conclusions: Our findings suggest that PD-L1 expression is at least in part regulated by miRNAs and may also suggest potential miRNAs to serve as biomarkers and therapeutic targets for cancer immunotherapy.
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Sousa, Marco Antônio Perpétuo de, Flavia Regina Florêncio de Athayde, Mariângela Bueno Cordeiro Maldonado, Andressa Oliveira de Lima, Marina Rufino S. Fortes, and Flavia Lombardi Lopes. "Single nucleotide polymorphisms affect miRNA target prediction in bovine." PLOS ONE 16, no. 4 (April 21, 2021): e0249406. http://dx.doi.org/10.1371/journal.pone.0249406.
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Single nucleotide polymorphisms (SNPs) can have significant effects on phenotypic characteristics in cattle. MicroRNAs (miRNAs) are small, non-coding RNAs that act as post-transcriptional regulators by binding them to target mRNAs. In the present study, we scanned ~56 million SNPs against 1,064 bovine miRNA sequences and analyzed, in silico, their possible effects on target binding prediction, primary miRNA formation, association with QTL regions and the evolutionary conservation for each SNP locus. Following target prediction, we show that 71.6% of miRNA predicted targets were altered as a consequence of SNPs located within the seed region of the mature miRNAs. Next, we identified variations in the Minimum Free Energy (MFE), which represents the capacity to alter molecule stability and, consequently, miRNA maturation. A total of 48.6% of the sequences analyzed showed values within those previously reported as sufficient to alter miRNA maturation. We have also found 131 SNPs in 46 miRNAs, with altered target prediction, occurring in QTL regions. Lastly, analysis of evolutionary conservation scores for each SNP locus suggested that they have a conserved biological function through the evolutionary process. Our results suggest that SNPs in microRNAs have the potential to affect bovine phenotypes and could be of great value for genetic improvement studies, as well as production.
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Mellis, David, and Andrea Caporali. "MicroRNA-based therapeutics in cardiovascular disease: screening and delivery to the target." Biochemical Society Transactions 46, no. 1 (December 1, 2017): 11–21. http://dx.doi.org/10.1042/bst20170037.
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MicroRNAs (miRNAs) are small non-coding RNAs of ∼22 nucleotides, which have increasingly been recognized as potent post-transcriptional regulators of gene expression. MiRNA targeting is defined by the complementarities between positions 2–8 of miRNA 5′-end with generally the 3′-untranslated region of target mRNAs (messenger RNAs). The capacity of miRNAs to simultaneously inhibit many different mRNAs allows for an amplification of biological responses. Hence, miRNAs are extremely attractive targets for therapeutic regulation in several diseases, including cardiovascular. Novel approaches are emerging to identify the miRNA functions in cardiovascular biology processes and to improve miRNA delivery in the heart and vasculature. In the present study, we provide an overview of current studies of miRNA functions in cardiovascular cells by the use of high-content screening. We also discuss the challenge to achieve a safe and targeted delivery of miRNA therapeutics in cardiovascular cells.
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Aisina, Dana, Raigul Niyazova, Shara Atambayeva, and Anatoliy Ivashchenko. "Prediction of clusters of miRNA binding sites in mRNA candidate genes of breast cancer subtypes." PeerJ 7 (November 13, 2019): e8049. http://dx.doi.org/10.7717/peerj.8049.
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The development of breast cancer (BC) subtypes is controlled by distinct sets of candidate genes, and the expression of these genes is regulated by the binding of their mRNAs with miRNAs. Predicting miRNA associations and target genes is thus essential when studying breast cancer. The MirTarget program identifies the initiation of miRNA binding to mRNA, the localization of miRNA binding sites in mRNA regions, and the free energy from the binding of all miRNA nucleotides with mRNA. Candidate gene mRNAs have clusters (miRNA binding sites with overlapping nucleotide sequences). mRNAs of EPOR, MAZ and NISCH candidate genes of the HER2 subtype have clusters, and there are four clusters in mRNAs of MAZ, BRCA2 and CDK6 genes. Candidate genes of the triple-negative subtype are targets for multiple miRNAs. There are 11 sites in CBL mRNA, five sites in MMP2 mRNA, and RAB5A mRNA contains two clusters in each of the three sites. In SFN mRNA, there are two clusters in three sites, and one cluster in 21 sites. Candidate genes of luminal A and B subtypes are targets for miRNAs: there are 21 sites in FOXA1 mRNA and 15 sites in HMGA2 mRNA. There are clusters of five sites in mRNAs of ITGB1 and SOX4 genes. Clusters of eight sites and 10 sites are identified in mRNAs of SMAD3 and TGFB1 genes, respectively. Organizing miRNA binding sites into clusters reduces the proportion of nucleotide binding sites in mRNAs. This overlapping of miRNA binding sites creates a competition among miRNAs for a binding site. From 6,272 miRNAs studied, only 29 miRNAs from miRBase and 88 novel miRNAs had binding sites in clusters of target gene mRNA in breast cancer. We propose using associations of miRNAs and their target genes as markers in breast cancer subtype diagnosis.
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Luo, Jiawei, Cong Huang, and Pingjian Ding. "A Meta-Path-Based Prediction Method for Human miRNA-Target Association." BioMed Research International 2016 (2016): 1–9. http://dx.doi.org/10.1155/2016/7460740.
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MicroRNAs (miRNAs) are short noncoding RNAs that play important roles in regulating gene expressing, and the perturbed miRNAs are often associated with development and tumorigenesis as they have effects on their target mRNA. Predicting potential miRNA-target associations from multiple types of genomic data is a considerable problem in the bioinformatics research. However, most of the existing methods did not fully use the experimentally validated miRNA-mRNA interactions. Here, we developed RMLM and RMLMSe to predict the relationship between miRNAs and their targets. RMLM and RMLMSe are global approaches as they can reconstruct the missing associations for all the miRNA-target simultaneously and RMLMSe demonstrates that the integration of sequence information can improve the performance of RMLM. In RMLM, we use RM measure to evaluate different relatedness between miRNA and its target based on different meta-paths; logistic regression and MLE method are employed to estimate the weight of different meta-paths. In RMLMSe, sequence information is utilized to improve the performance of RMLM. Here, we carry on fivefold cross validation and pathway enrichment analysis to prove the performance of our methods. The fivefold experiments show that our methods have higher AUC scores compared with other methods and the integration of sequence information can improve the performance of miRNA-target association prediction.
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Faiza, Muniba, Khushnuma Tanveer, Saman Fatihi, Yonghua Wang, and Khalid Raza. "Comprehensive Overview and Assessment of microRNA Target Prediction Tools in Homo sapiens and Drosophila melanogaster." Current Bioinformatics 14, no. 5 (June 28, 2019): 432–45. http://dx.doi.org/10.2174/1574893614666190103101033.
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Background: MicroRNAs (miRNAs) are small non-coding RNAs that control gene expression at the post-transcriptional level through complementary base pairing with the target mRNA, leading to mRNA degradation and blocking translation process. Many dysfunctions of these small regulatory molecules have been linked to the development and progression of several diseases. Therefore, it is necessary to reliably predict potential miRNA targets. Objective: A large number of computational prediction tools have been developed which provide a faster way to find putative miRNA targets, but at the same time, their results are often inconsistent. Hence, finding a reliable, functional miRNA target is still a challenging task. Also, each tool is equipped with different algorithms, and it is difficult for the biologists to know which tool is the best choice for their study. Methods: We analyzed eleven miRNA target predictors on Drosophila melanogaster and Homo sapiens by applying significant empirical methods to evaluate and assess their accuracy and performance using experimentally validated high confident mature miRNAs and their targets. In addition, this paper also describes miRNA target prediction algorithms, and discusses common features of frequently used target prediction tools. Results: The results show that MicroT, microRNA and CoMir are the best performing tool on Drosopihla melanogaster; while TargetScan and miRmap perform well for Homo sapiens. The predicted results of each tool were combined in order to improve the performance in both the datasets, but any significant improvement is not observed in terms of true positives. Conclusion: The currently available miRNA target prediction tools greatly suffer from a large number of false positives. Therefore, computational prediction of significant targets with high statistical confidence is still an open challenge.
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Li, Shuxia, Zhihao Cheng, and Ming Peng. "Genome-wide identification of miRNAs targets involved in cold response in cassava." MAY 2020, no. 13(01): 2020 (May 20, 2020): 57–64. http://dx.doi.org/10.21475/poj.13.01.20.p2337.
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MicroRNAs (miRNAs) are recognized as essential transcriptional or post-transcriptional regulators, and play versatile roles in plants growth, development and stress responses. Cassava (Manihot esculenta) is a major root crop widely grown worldwide. Cold stress seriously affects cassava plants growth, development and yield. MiRNAs and their targets have been extensively studied in model plants, but a genome-wide identification of miRNAs’ targets is still lacking in cassava. In this study, two degradome libraries were constructed using cold-treated and control cassava seedlings to identify the roles of miRNAs and their targets in response to cold stress. Following high-throughput sequencing and comparing with miRNA database, degradome data allowed us to identify a total of 151 non-redundant miRNA-target pairs. We revealed that ~ 42% of miRNA targets are conserved across plant species. However, 83 novel miRNA targets were identified in the two libraries. Gene ontology analyses showed that many target genes involved in cellular and metabolic process. In addition, 12 miRNAs and 31 corresponding targets of them were further found to be involved in cold stress response. Particularly, miR159, 164 and 396 participated in cold stress response by up-regulating certain transcription factors that were involved in the regulation of downstream gene expression. The work helps identifing cold-responsive miRNA targets in cassava and increases the number of novel targets involved in cold stress response. Furthermore, the findings of this study might provide valuable reference and new insights for understanding the functions of miRNA in stress response in plants.
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Taguchi, Y.-h. "Correlation between miRNA-targeted-gene promoter methylation and miRNA regulation of target genes." F1000Research 2 (March 27, 2013): 21. http://dx.doi.org/10.12688/f1000research.2-21.v2.
Full textAbstract:
Background miRNA regulation of target genes and promoter methylation are known to be the primary mechanisms underlying the epigenetic regulation of gene expression. However, how these two processes cooperatively regulate gene expression has not been extensively studied. Methods Gene expression and promoter methylation profiles of 271 distinct human cell lines were obtained from gene expression omnibus. P-values that describe both miRNA-targeted-gene promoter methylaion and miRNA regulation of target genes were computed using the MiRaGE method proposed recently by the author.Results Significant changes in promoter methylation were associated with miRNA targeting. It was also found that miRNA-targeted-gene promoter hypomethylation was related to differential target gene expression; the genes with miRNA-targeted-gene promoter hypomethylation were downregulated during cell senescence and upregulated during cellular differentiation. Promoter hypomethylation was especially enhanced for genes targeted by miR-548 miRNAs, which are non-conserved, primate-specific miRNAs that are typically expressed at lower levels than the frequently investigated conserved miRNAs.Conclusions It was found that promoter methylation was affected by miRNA targeting. Furthermore, miRNA-targeted-gene promoter hypomethylation is suggested to facilitate gene regulation by miRNAs that are not strongly expressed (e.g., miR-548 miRNAs).
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Naderi, Elnaz, Mehdi Mostafaei, Akram Pourshams, and Ashraf Mohamadkhani. "Network of microRNAs-mRNAs Interactions in Pancreatic Cancer." BioMed Research International 2014 (2014): 1–8. http://dx.doi.org/10.1155/2014/534821.
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Background.MicroRNAs are small RNA molecules that regulate the expression of certain genes through interaction with mRNA targets and are mainly involved in human cancer. This study was conducted to make the network of miRNAs-mRNAs interactions in pancreatic cancer as the fourth leading cause of cancer death.Methods.56 miRNAs that were exclusively expressed and 1176 genes that were downregulated or silenced in pancreas cancer were extracted from beforehand investigations. MiRNA–mRNA interactions data analysis and related networks were explored using MAGIA tool and Cytoscape 3 software. Functional annotations of candidate genes in pancreatic cancer were identified by DAVID annotation tool.Results.This network is made of 217 nodes for mRNA, 15 nodes for miRNA, and 241 edges that show 241 regulations between 15 miRNAs and 217 target genes. The miR-24 was the most significantly powerful miRNA that regulated series of important genes. ACVR2B, GFRA1, and MTHFR were significant target genes were that downregulated.Conclusion.Although the collected previous data seems to be a treasure trove, there was no study simultaneous to analysis of miRNAs and mRNAs interaction. Network of miRNA-mRNA interactions will help to corroborate experimental remarks and could be used to refine miRNA target predictions for developing new therapeutic approaches.
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Li, Yingyuan, Wulin Tan, Fang Ye, Faling Xue, Shaowei Gao, Wenqi Huang, and Zhongxing Wang. "Identification of microRNAs and genes as biomarkers of atrial fibrillation using a bioinformatics approach." Journal of International Medical Research 47, no. 8 (June 20, 2019): 3580–89. http://dx.doi.org/10.1177/0300060519852235.
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Objective We aimed to explore potential microRNAs (miRNAs) and target genes related to atrial fibrillation (AF). Methods Data for microarrays GSE70887 and GSE68475, both of which include AF and control groups, were downloaded from the Gene Expression Omnibus database. Differentially expressed miRNAs between AF and control groups were identified within each microarray, and the intersection of these two sets was obtained. These miRNAs were mapped to target genes in the miRNet database. Functional annotation and enrichment analysis of these target genes was performed in the DAVID database. The protein-protein interaction (PPI) network from the STRING database and the miRNA-target-gene network were merged into a PPI-miRNA network using Cytoscape software. Modules of this network containing miRNAs were detected and further analyzed. Results Ten differentially expressed miRNAs and 1520 target genes were identified. Three PPI-miRNA modules were constructed, which contained miR-424, miR-15a, miR-542-3p, and miR-421 as well as their target genes, CDK1, CDK6, and CCND3. Conclusion The identified miRNAs and genes may be related to the pathogenesis of AF. Thus, they may be potential biomarkers for diagnosis and targets for treatment of AF.
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Calderari, Sophie, Malika R. Diawara, Alois Garaud, and Dominique Gauguier. "Biological roles of microRNAs in the control of insulin secretion and action." Physiological Genomics 49, no. 1 (January 1, 2017): 1–10. http://dx.doi.org/10.1152/physiolgenomics.00079.2016.
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microRNAs (miRNAs) are intracellular and circulating molecular components contributing to genome expression control through binding mRNA targets, which generally results in downregulated mRNA expression. One miRNA can target several mRNAs, and one transcript can be targeted by several miRNAs, resulting in complex fine-tuning of regulation of gene networks and signaling pathways. miRNAs regulate metabolism, adipocyte differentiation, pancreatic development, β-cell mass, insulin biosynthesis, secretion, and signaling, and their role in diabetes and obesity is emerging. Their pathophysiological effects are essentially dependent on cellular coexpression with their mRNA targets, which can show tissue-specific transcriptional responses to disease conditions and environmental challenges. Current knowledge of miRNA biology and their impact on the pathogenesis of diabetes and obesity is based on experimental data documenting miRNA expression generally in single tissue types that can be correlated with expression of target mRNAs to integrate miRNA in functional pathways and gene networks. Here we present results from the most significant studies dealing with miRNA function in liver, fat, skeletal muscle, and endocrine pancreas and their implication in diabetes and obesity.
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Hu, Zihua, and Andrew E. Bruno. "The Influence of 3′UTRs on MicroRNA Function Inferred from Human SNP Data." Comparative and Functional Genomics 2011 (2011): 1–9. http://dx.doi.org/10.1155/2011/910769.
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MicroRNAs (miRNAs) regulate gene expression posttranscriptionally. Although previous efforts have demonstrated the functional importance of target sites on miRNAs, little is known about the influence of the rest of 3′ untranslated regions (3′UTRs) of target genes on microRNA function. We conducted a genome-wide study and found that the entire 3′UTR sequences could also play important roles on miRNA function in addition to miRNA target sites. This was evidenced by the fact that human single nucleotide polymorphisms (SNPs) on both seed target region and the rest of 3′UTRs of miRNA target genes were under significantly stronger negative selection, when compared to non-miRNA target genes. We also discovered that the flanking nucleotides on both sides of miRNA target sites were subject to moderate strong selection. A local sequence region of ~67 nucleotides with symmetric structure is herein defined. Additionally, from gene expression analysis, we found that SNPs and miRNA target sites on target sequences may interactively affect gene expression.
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Kandhavelu, Jeyalakshmi, Kumar Subramanian, Amber Khan, Aadilah Omar, Paul Ruff, and Clement Penny. "Computational Analysis of miRNA and their Gene Targets Significantly Involved in Colorectal Cancer Progression." MicroRNA 8, no. 1 (November 27, 2018): 68–75. http://dx.doi.org/10.2174/2211536607666180803100246.
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Background:Globally, colorectal cancer (CRC) is the third most common cancer in women and the fourth most common cancer in men. Dysregulation of small non-coding miRNAs have been correlated with colon cancer progression. Since there are increasing reports of candidate miRNAs as potential biomarkers for CRC, this makes it important to explore common miRNA biomarkers for colon cancer. As computational prediction of miRNA targets is a critical initial step in identifying miRNA: mRNA target interactions for validation, we aim here to construct a potential miRNA network and its gene targets for colon cancer from previously reported candidate miRNAs, inclusive of 10 up- and 9 down-regulated miRNAs from tissues; and 10 circulatory miRNAs. </P><P> Methods: The gene targets were predicted using DIANA-microT-CDS and TarBaseV7.0 databases. Each miRNA and its targets were analyzed further for colon cancer hotspot genes, whereupon DAVID analysis and mirPath were used for KEGG pathway analysis.Results:We have predicted 874 and 157 gene targets for tissue and serum specific miRNA candidates, respectively. The enrichment of miRNA revealed that particularly hsa-miR-424-5p, hsa-miR-96-5p, hsa-miR-1290, hsa-miR-224, hsa-miR-133a and has-miR-363-3p present possible targets for colon cancer hallmark genes, including BRAF, KRAS, EGFR, APC, amongst others. DAVID analysis of miRNA and associated gene targets revealed the KEGG pathways most related to cancer and colon cancer. Similar results were observed in mirPath analysis. A new insight gained in the colon cancer network pathway was the association of hsa-mir-133a and hsa-mir-96-5p with the PI3K-AKT signaling pathway. In the present study, target prediction shows that while hsa-mir-424-5p has an association with mostly 10 colon cancer hallmark genes, only their associations with MAP2 and CCND1 have been experimentally validated.These miRNAs and their targets require further evaluation for a better understanding of their associations, ultimately with the potential to develop novel therapeutic targets.
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Tang, Qi, Haozhe Lv, Qimeng Li, Xiaoyue Zhang, Le Li, Jie Xu, Fengkai Wu, Qingjun Wang, Xuanjun Feng, and Yanli Lu. "Characteristics of microRNAs and Target Genes in Maize Root under Drought Stress." International Journal of Molecular Sciences 23, no. 9 (April 29, 2022): 4968. http://dx.doi.org/10.3390/ijms23094968.
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Maize (Zea mays) is an important multi-functional crop. The growth and yield of maize are severely affected by drought stress. Previous studies have shown that microRNAs (miRNAs) in maize play important roles in response to abiotic stress; however, their roles in response to drought stress in maize roots is unclear. In our study, we found 375 miRNAs in the roots of 16 inbred lines. Of the 16 lines, zma-MIR168, zma-MIR156, and zma-MIR166 were highly expressed, whereas zma-MIR399, zma-MIR2218, and zma-MIR2275 exhibited low expression levels. The expression patterns of miRNA in parental lines and their derived RILs are different. Over 50% of miRNAs exhibited a lower expression in recombinant inbred lines than in parents. The expression of 50 miRNAs was significantly altered under water stress (WS) in at least three inbred lines, and the expression of miRNAs in drought-tolerant lines changed markedly. To better understand the reasons for miRNA response to drought, the degree of histone modifications for miRNA genes was estimated. The methylation level of H3K4 and H3K9 in miRNA precursor regions changed more noticeably after WS, but no such phenomenon was seen for DNA methylation and m6A modification. After the prediction of miRNA targets using psRNATarget and psRobot, we used correlation analysis and qRT-PCR to further investigate the relationship between miRNAs and target genes. We found that 87 miRNA–target pairs were significantly negatively correlated. In addition, a weighted gene co-expression network analysis using miRNAs, as well as their predicted targets, was conducted to reveal that miR159, miR394, and miR319 may be related to maize root growth. The results demonstrated that miRNAs might play essential roles in the response to drought stress.
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Fei, Yuhan, Yiyang Mao, Chengji Shen, Rui Wang, Hongsheng Zhang, and Ji Huang. "WPMIAS: Whole-degradome-based Plant MicroRNA–target Interaction Analysis Server." Bioinformatics 36, no. 6 (November 6, 2019): 1937–39. http://dx.doi.org/10.1093/bioinformatics/btz820.
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Abstract Summary A critical aspect for exploring the biological function of a microRNA (miRNA) lies on exact detection and validation of its target mRNAs. However, no convenient and efficient web-based server is available for plant biologists to identify the experimentally verified target mRNAs of miRNAs. In this work, we built a comprehensive web-based platform for miRNA–target analysis, named as Whole-degradome-based Plant MiRNA–target Interaction Analysis Server (WPMIAS), for validation of predicted interactions of miRNAs and their target mRNAs (MTIs) by user-submitted data or all available pre-loaded degradome data. Besides, the server can construct degradome-based miRNA regulatory networks (MRNs) based on the validated MTIs to help study the functions and relations among miRNAs and target mRNAs. WPMIAS is also suitable for other small RNAs (sRNAs), such as 21-nt phased siRNAs and natural antisense siRNAs, which direct cleavage of target mRNAs. Currently, WPMIAS supports 68 plant species with 189 cDNA and 271 pre-loaded plant degradome datasets. The user can identify all validated MTIs by analyzing all degradome data at a time and understand when and where MTIs take place and their cleavage levels. With the data obtained from WPMIAS, the user can build a plant miRNA–target map, where it is convenient to find interesting research ideas on miRNAs. In summary, WPMIAS is able to support a comprehensive web-based plant miRNA–target analysis and expected to greatly promote future research on plant miRNAs. Availability and implementation It can be freely accessed at https://cbi.njau.edu.cn/WPMIAS/. Supplementary information Supplementary data are available at Bioinformatics online.
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Shao, Tingting, Guangjuan Wang, Hong Chen, Yunjin Xie, Xiyun Jin, Jing Bai, Juan Xu, et al. "Survey of miRNA-miRNA cooperative regulation principles across cancer types." Briefings in Bioinformatics 20, no. 5 (May 25, 2018): 1621–38. http://dx.doi.org/10.1093/bib/bby038.
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AbstractCooperative regulation among multiple microRNAs (miRNAs) is a complex type of posttranscriptional regulation in human; however, the global view of the system-level regulatory principles across cancers is still unclear. Here, we investigated miRNA-miRNA cooperative regulatory landscape across 18 cancer types and summarized the regulatory principles of miRNAs. The miRNA-miRNA cooperative pan-cancer network exhibited a scale-free and modular architecture. Cancer types with similar tissue origins had high similarity in cooperative network structure and expression of cooperative miRNA pairs. In addition, cooperative miRNAs showed divergent properties, including higher expression, greater expression variation and a stronger regulatory strength towards targets and were likely to regulate cancer hallmark-related functions. We found a marked rewiring of miRNA-miRNA cooperation between various cancers and revealed conserved and rewired network miRNA hubs. We further identified the common hubs, cancer-specific hubs and other hubs, which tend to target known anticancer drug targets. Finally, miRNA cooperative modules were found to be associated with patient survival in several cancer types. Our study highlights the potential of pan-cancer miRNA-miRNA cooperative regulation as a novel paradigm that may aid in the discovery of tumorigenesis mechanisms and development of anticancer drugs.
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Shinde, Santosh, and Utpal Bhadra. "MicroRNA Gene Interaction in Amyotrophic Lateral Sclerosis Dataset." Dataset Papers in Science 2014 (June 30, 2014): 1–24. http://dx.doi.org/10.1155/2014/780726.
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All microRNAs (miRNAs) in amyotrophic lateral sclerosis (ALS) study were collected from public databases such as miRBase, mir2Disease, and Human miRNA and Disease Database (HMDD). These miRNA datasets were used for target identification; these sets of miRNAs were expressed in brain specific parts of brain such as midbrain, cerebellum, frontal cortex, and hippocampus. Gene’s information and sequences were collected from NCBI and KEGG databases. All miRNAs were used for target prediction against 35 ALS associated genes. Three programs were used for target identification, namely, miRanda, TargetScan, and PicTar. The dataset contained information about miRNA targets sites identified by each program. Intersection studies of three programs such as miRanda versus TargetScan, miRanda versus PicTar, and TargetScan versus PicTar were carried out with all datasets. Target sites identified by each program were further explored for distribution of target sites across 35 genes in 5′ UTR, CDS, and 3′ UTR for miRNAs expressed in midbrain, cerebellum, frontal cortex, and hippocampus as predicted. Dataset was also used for calculation of multiplicity and coopretivity; this information was then used for construction of complex gene-microRNA interaction map.