The evolving role of microRNAs in animal gene expression

MicroRNAs (miRNAs) constitute an abundant family of 22-nucleotide RNAs that base-pair to target mRNAs and typically inhibit their expression. To assess the global impact of animal miRNAs on gene regulation, the expression of predicted targets and their cognate miRNAs was extensively analyzed in mammals and Drosophila. In general, targets are co-expressed at relatively low or undetectable levels in the same tissues as the miRNAs predicted to regulate them. Additionally, genes that are highly co-expressed with miRNAs usually lack target sites. The authors conclude that many animal genes are under evolutionary pressure to maintain or avoid complementary sites to miRNAs. Thus, the miRNA pathway broadly contributes to the complex gene regulatory networks that shape animal tissue development and identity.     

Conserved seed pairing, often flanked by adenosines, indicates that thousands of human genes are microRNA targets

We predict regulatory targets of vertebrate microRNAs (miRNAs) by identifying mRNAs with conserved complementarity to the seed (nucleotides 2-7) of the miRNA. An overrepresentation of conserved adenosines flanking the seed complementary sites in mRNAs indicates that primary sequence determinants can supplement base pairing to specify miRNA target recognition. In a four-genome analysis of 3' UTRs, approximately 13,000 regulatory relationships were detected above the estimate of false-positive predictions, thereby implicating as miRNA targets more than 5300 human genes, which represented 30% of our gene set. Targeting was also detected in open reading frames. In sum, well over one third of human genes appear to be conserved miRNA targets.     

红花microRNAs靶基因的生物信息学预测

miRNA是一类动、植物细胞中负责转录后调控的非编码RNA,植物的miRNA通常来源于具有茎环结构的单链RNA前体上,加工成熟后同ARGONAUTE转变成蛋白复合体结构,通过结合并沉默蛋白合成模板的方式关闭完整的基因表达网络。由于miRNA存在组织表达的特异性,且miRNA在植物中的表达高度保守。通过高通量测序获得的红花(Carthamus tinctorious L.)miRNA序列信息与红花转录EST数据库比对,通过靶基因预测筛选,对属于104个家族的173个红花miRNA进行预测,其中得到109个miRNAs对应调控的385个红花靶基因。并且通过Nr基因注释表明多数红花miRNA的靶基因编码包括调控细胞生长发育、信号转导及新陈代谢等相关的功能蛋白。     

Host microRNAs exhibit differential propensity to interact with SARS-CoV-2 and variants of concern

A significant number of SARS-CoV-2-infected individuals naturally overcome viral infection, suggesting the existence of a potent endogenous antiviral mechanism. As an innate defense mechanism, microRNA (miRNA) pathways in mammals have evolved to restrict viruses, besides regulating endogenous mRNAs. In this study, we systematically examined the complete repertoire of human miRNAs for potential binding sites on SARS-CoV-2 Wuhan-Hu-1, Beta, Delta, and Omicron. Human miRNA and viral genome interaction were analyzed using RNAhybrid 2.2 with stringent parameters to identify highly bonafide miRNA targets. Using publicly available data, we filtered for miRNAs expressed in lung epithelial cells/tissue and oral keratinocytes, concentrating on the miRNAs that target SARS-CoV-2 S protein mRNAs. Our results show a significant loss of human miRNA and SARS-CoV-2 interactions in Omicron (130 miRNAs) compared to Wuhan-Hu-1 (271 miRNAs), Beta (279 miRNAs), and Delta (275 miRNAs). In particular, hsa-miR-3150b-3p and hsa-miR-4784 show binding affinity for S protein of Wuhan strain but not Beta, Delta, and Omicron. Loss of miRNA binding sites on N protein was also observed for Omicron. Through Ingenuity Pathway Analysis (IPA), we examined the experimentally validated and highly predicted functional role of these miRNAs. We found that hsa-miR-3150b-3p and hsa-miR-4784 have several experimentally validated or highly predicted target genes in the Toll-like receptor, IL-17, Th1, Th2, interferon, and coronavirus pathogenesis pathways. Focusing on the coronavirus pathogenesis pathway, we found that hsa-miR-3150b-3p and hsa-miR-4784 are highly predicted to target MAPK13. Exploring miRNAs to manipulate viral genome/gene expression can provide a promising strategy with successful outcomes by targeting specific VOCs.     

HomoTarget: A new algorithm for prediction of microRNA targets in Homo sapiens

MiRNAs play an essential role in the networks of gene regulation by inhibiting the translation of target mRNAs. Several computational approaches have been proposed for the prediction of miRNA target-genes. Reports reveal a large fraction of under-predicted or falsely predicted target genes. Thus, there is an imperative need to develop a computational method by which the target mRNAs of existing miRNAs can be correctly identified. In this study, combined pattern recognition neural network (PRNN) and principle component analysis (PCA) architecture has been proposed in order to model the complicated relationship between miRNAs and their target mRNAs in humans. The results of several types of intelligent classifiers and our proposed model were compared, showing that our algorithm outperformed them with higher sensitivity and specificity. Using the recent release of the mirBase database to find potential targets of miRNAs, this model incorporated twelve structural, thermodynamic and positional features of miRNA:mRNA binding sites to select target candidates.     

miRTar Hunter: A prediction system for identifying human microRNA target sites

MicroRNAs (miRNAs) are important regulators of gene expression and play crucial roles in many biological processes including apoptosis, differentiation, development, and tumorigenesis. Recent estimates suggest that more than 50% of human protein coding genes may be regulated by miRNAs and that each miRNA may bind to 300–400 target genes. Approximately 1,000 human miRNAs have been identified so far with each having up to hundreds of unique target mRNAs. However, the targets for a majority of these miRNAs have not been identified due to the lack of large-scale experimental detection techniques. Experimental detection of miRNA target sites is a costly and time-consuming process, even though identification of miRNA targets is critical to unraveling their functions in various biological processes. To identify miRNA targets, we developed miRTar Hunter, a novel computational approach for predicting target sites regardless of the presence or absence of a seed match or evolutionary sequence conservation. Our approach is based on a dynamic programming algorithm that incorporates more sequence-specific features and reflects the properties of various types of target sites that determine diverse aspects of complementarities between miRNAs and their targets. We evaluated the performance of our algorithm on 532 known human miRNA:target pairs and 59 experimentally-verified negative miRNA:target pairs, and also compared our method with three popular programs for 481 miRNA:target pairs. miRTar Hunter outperformed three popular existing algorithms in terms of recall and precision, indicating that our unique scheme to quantify the determinants of complementary sites is effective at detecting miRNA targets. miRTar Hunter is now available at http://203.230.194.162/~kbkim.     

MicroRNAs and their targets: recognition, regulation and an emerging reciprocal relationship

MicroRNAs (miRNAs) have emerged as key gene regulators in diverse biological pathways. These small non-coding RNAs bind to target sequences in mRNAs, typically resulting in repressed gene expression. Several methods are now available for identifying miRNA target sites, but the mere presence of an miRNA-binding site is insufficient for predicting target regulation. Regulation of targets by miRNAs is subject to various levels of control, and recent developments have presented a new twist; targets can reciprocally control the level and function of miRNAs. This mutual regulation of miRNAs and target genes is challenging our understanding of the gene-regulatory role of miRNAs in vivo and has important implications for the use of these RNAs in therapeutic settings.     

miRNA与其靶mRNA的相互作用：绑定位点的质量与数量特征的整合计算分析

miRNAs通过完全或不完全的碱基互补绑定到信使RNA(mRNA)上,通过抑制翻译或者直接导致mRNA降解的方式来调节靶基因的表达．为了研究miRNAs在转录水平上面的调控作用,两种人类基因组中组织特异的miRNAs(miR-1和miR-124)被转染到HeLa细胞中,微阵列(microarray)分析转染前后细胞中各基因mRNA表达水平变化情况的结果表明：动物基因组中靶基因与miRNAs不完全的碱基互补也会导致mRNA的直接降解．通过分析实验得到的mRNA表达水平变化数据,发现这相同miRNA的不同靶基因mRNA表达水平的下调倍数有着明显的差别,推测这些靶基因mRNA序列本身存在某些影响其受调节程度的因素．为此,提取和分析这些靶基因mRNA的序列特征,通过对这些序列特征与mRNA表达水平下调数据进行统计相关分析,最终发现,miRNA靶基因受调节的程度与以下几个因素相关联：mRNA序列中miRNA靶位点的个数,靶位点与miRNA序列碱基互补的程度,以及绑定后形成二级结构的稳定程度(即最低自由能的大小)．在此基础上,初步建立起一个多因子作用下的miRNA 靶基因mRNA表达水平下调程度模型,分析表明：该模型在一定程度上可以反映了部分序列特征对于miRNA靶基因mRNA表达水平下调程度的影响．