MicroRNAs研究方法与技术

MicroRNAs简称miRNAs(微小RNAs),是真核生物、原核生物以及病毒中由非编码蛋白基因转录的初级microRNAs加工成的调控因子.在转录后水平和蛋白质翻译水平,microRNAs通过降解或翻译抑制甚至激活来调控靶mRNA.实验和计算机方法已应用于microRNAs和靶基因的鉴定.大规模测序技术使得microRNAs在不同物种的多样性分析得以实现.着重介绍microRNAs、靶基因及其功能研究的实验技术和计算机方法,以及基于microRNAs的保守性,借助模式生物中已知的microRNAs,研究其在其他生物中的功能和作用.     

基于microRNA共调控网络的microRNA调控机制研究

已有研究通过计算和实验的手段,证明了不同的microRNA(miRNA)通过相互之间的合作,来共同调控它们所共有的靶基因。对miRNA之间这种合作行为的特性的研究,能够帮助我们更好的理解miRNA的调控机理。本文建立了一个网络来描述miRNA之间的合作关系,并通过对该网络的分析,得出了四点关于miRNA调控机制的性质。第一,基因靶标数目越多的miRNA倾向于与越多的miRNA伙伴进行合作。第二,进化上保守的miRNA所具有的共调控伙伴的数目显著多于非保守的miRNA。第三,以上的性质是跨物种的存在的(人与小鼠)。第四,miRNA与蛋白质在系统层面性质存在一定的相似。     

Target Repression Induced by Endogenous microRNAs: Large Differences,Small Effects

MicroRNAs are small RNAs that regulate protein levels. It is commonly assumed that the expression level of a microRNA is directly correlated with its repressive activity – that is, highly expressed microRNAs will repress their target mRNAs more. Here we investigate the quantitative relationship between endogenous microRNA expression and repression for 32 mature microRNAs in Drosophila melanogaster S2 cells. In general, we find that more abundant microRNAs repress their targets to a greater degree. However, the relationship between expression and repression is nonlinear, such that a 10-fold greater microRNA concentration produces only a 10% increase in target repression. The expression/repression relationship is the same for both dominant guide microRNAs and minor mature products (so-called passenger strands/microRNA* sequences). However, we find examples of microRNAs whose cellular concentrations differ by several orders of magnitude, yet induce similar repression of target mRNAs. Likewise, microRNAs with similar expression can have very different repressive abilities. We show that the association of microRNAs with Argonaute proteins does not explain this variation in repression. The observed relationship is consistent with the limiting step in target repression being the association of the microRNA/RISC complex with the target site. These findings argue that modest changes in cellular microRNA concentration will have minor effects on repression of targets.     

Gene silencing by microRNAs: contributions of translational repression and mRNA decay

Despite their widespread roles as regulators of gene expression, important questions remain about target regulation by microRNAs. Animal microRNAs were originally thought to repress target translation, with little or no influence on mRNA abundance, whereas the reverse was thought to be true in plants. Now, however, it is clear that microRNAs can induce mRNA degradation in animals and, conversely, translational repression in plants. Recent studies have made important advances in elucidating the relative contributions of these two different modes of target regulation by microRNAs. They have also shed light on the specific mechanisms of target silencing, which, although it differs fundamentally between plants and animals, shares some common features between the two kingdoms.     

microRNA target predictions across seven Drosophila species and comparison to mammalian targets

microRNAs are small noncoding genes that regulate the protein production of genes by binding to partially complementary sites in the mRNAs of targeted genes. Here, using our algorithm PicTar, we exploit cross-species comparisons to predict, on average, 54 targeted genes per microRNA above noise in Drosophila melanogaster. Analysis of the functional annotation of target genes furthermore suggests specific biological functions for many microRNAs. We also predict combinatorial targets for clustered microRNAs and find that some clustered microRNAs are likely to coordinately regulate target genes. Furthermore, we compare microRNA regulation between insects and vertebrates. We find that the widespread extent of gene regulation by microRNAs is comparable between flies and mammals but that certain microRNAs may function in clade-specific modes of gene regulation. One of these microRNAs (miR-210) is predicted to contribute to the regulation of fly oogenesis. We also list specific regulatory relationships that appear to be conserved between flies and mammals. Our findings provide the most extensive microRNA target predictions in Drosophila to date, suggest specific functional roles for most microRNAs, indicate the existence of coordinate gene regulation executed by clustered microRNAs, and shed light on the evolution of microRNA function across large evolutionary distances. All predictions are freely accessible at our searchable Web site http://pictar.bio.nyu.edu.     

Distinctive microRNA signature associated of neoplasms with the Wnt/β-catenin signaling pathway

As the crucial biological regulators, microRNAs that act by suppressing their target genes are involved in a variety of pathophysiological processes. It is generally accepted that microRNAs are often dysregulated in many types of neoplasm and other human diseases. In neoplasm, microRNAs may function as oncogenes or tumor suppressors. As constitutive activation of the Wnt signaling pathway is a common feature of neoplasm and contributes to its development, progression and metastasis in various cancers, numerous studies have revealed that microRNA-mediated gene regulation are interconnected with the Wnt/β-catenin signaling pathway, forming a Wnt/β-catenin–microRNA regulatory network, which is critical to successful targeting of the Wnt/β-catenin pathway for oncotherapy. In this review, we aim to accumulate recent advances on microRNAs that work in tandem with Wnt/β-catenin signaling in tumorigenesis, with particular focus on how microRNAs affect Wnt/β-catenin activity as well as how microRNAs are regulated through the Wnt/β-catenin pathway.     

A high-throughput approach for associating MicroRNAs with their activity conditions.

Plant microRNAs (miRNAs) are short RNA sequences that bind to target mRNAs and change their expression levels by redirecting their stabilities and marking them for cleavage. In Arabidopsis thaliana, microRNAs have been shown to regulate development and are believed to impact expression both under various conditions, such as stress and stimuli, as well as in specific tissue types. We present a high throughput approach for associating between microRNAs and conditions in which they act, using novel statistical and algorithmic techniques. Our new tool, miRNAXpress, at first computes a (binary) matrix T denoting the potential targets of microRNAs. Then, using T and an additional predefined matrix X indicating expression of genes under various conditions, it produces a new matrix that predicts associations between microRNAs and the conditions in which they act. Thus, the program comprises two main modules that work in tandem to compute the desired output. The first is an efficient target prediction engine that predicts mRNA targets of query microRNAs by evaluating the optimal duplex that could be formed between the two: given a short query RNA, a long target RNA, and a predefined energy cut-off threshold, the program finds and reports all putative binding sites of the query RNA in the target RNA with hybridization energy bounded by the predefined threshold. The second module realizes an association operation that is computed by a method which relies on an efficient t-test to compute the associations. The calculation of the matrix of microRNAs and their potential targets is the computationally intensive part of the work done by miRNAXpress, and therefore an efficient algorithm for this portion facilitates the entire process. Thus, the target prediction engine is based on an efficient approximate hybridization search algorithm whose efficiency is the result of utilizing the sparsity of the search space without sacrificing the optimality of the results. The time complexity of this algorithm is almost linear in the size of a sparse set of locations where base-pairs are stacked at a height of three or more. Thus miRNAXpress is a novel tool for associating between microRNAs and the conditions in which they act. We employed it to conduct a study, using the plant Arabidopsis thaliana as our model organism. By applying miRNAXpress to 98 microRNAs and 380 conditions, some biologically interesting and statistically strong relations were discovered. For example, mir159C activity is possibly a factor in the misresponse of nph4 mutants to phototropic stimulations.     

Combinatorial microRNAs: Working together to make a difference

MicroRNAs regulate gene networks and therefore are inherently complex. MicroRNAs themselves function in networks of other microRNAs, some of which are co-expressed from the same locus. To better understand the interplay among microRNAs that underlies their functions, we examined the potential of combinatorial effects of endogenously and exogenously co-expressed microRNAs. In this review, we first distill the similarities and differences between three microRNA families that function in cell division, miR-16, miR-34a and miR-106b, with emphasis on their exquisite phenotypic diversity. Given that the microRNAs affect cell cycle progression via distinct targets, we tested for phenotypic synergism among them. Furthermore, we investigate target regulation by individual and pooled microRNAs to gain insight into interactions among microRNAs co-expressed from the same chromosomal locus. The ability of microRNAs to modulate multiple genes within a molecular pathway engenders a novel way of thinking about targeting pathways: instead of a one-inhibitor-one-target model, multiple components in a pathway can be modulated by a microRNA resulting in a potent yet reversible inhibition of the pathway. To fully realize this potential, we need to understand how microRNAs function singly and in concert with each other.     

The effect of xenobiotics on microRNA expression in the rat liver

The effect of xenobiotics on microRNA expression in the rat liver has been investigated. Based on results of bioinformatics analysis several microRNAs that can interact with 3'-untranslated regions of cytochrome P450 (CYP) mRNAs have been selected. These included three microRNAs (miR-21, miR-221, miR-222) for CYP1A1 mRNA as a putative target and two microRNAs (miR-143, miR-152) for CYP2B1 mRNA as a putative target. Using the RT-PCR method, expression levels of these microRNAs have been detected in the liver of rats treated with inducers of CYP1A and CYP2B, benzo(a)pyrene (BP), phenobarbital (PB), and DDT. In rats treated with both BP and DDT the hepatic content of miR-21, miR-221 and miR-222 was 2?3 times lower than in the control animals, while ethoxyresorufin-O-deethylase (EROD) activity of CYP1A1 demonstrated a 5.5?8.7-fold increase. In PB-treated rats miR-143 expression remained unchanged, the level of miR-152 increased 2-fold, while pentoxyresorufin-O-deetylase (PROD) activity of CYP2B increased 10.5-fold. In the liver of DDT-treated rats PROD activity demonstrated a 20.8-fold increase; expression of miR-143 increased 2-fold, and miR-152 expression remained unchanged. Bioinformatics analysis of putative miR-target interactions showed that the selected microRNAs can potentially bind such target as AhR, ESR1, GR, CCND1, PTEN mRNAs. Thus, the expression profile of miR-21, miR-221, miR-222, miR-143, miR-152 may vary in dependence on the CYP inducer used. Analysis in silico has shown that besides genes encoding CYP1A/2B other genes including those involved in hormonal carcinogenesis should be considered as potential targets of the investigated microRNAs.     

Detection of simultaneous group effects in microRNA expression and related target gene sets

Expression levels of mRNAs are among other factors regulated by microRNAs. A particular microRNA can bind specifically to several target mRNAs and lead to their degradation. Expression levels of both, mRNAs and microRNAs, can be obtained by microarray experiments. In order to increase the power of detecting microRNAs that are differentially expressed between two different groups of samples, we incorporate expression levels of their related target gene sets. Group effects are determined individually for each microRNA, and by enrichment tests and global tests for target gene sets. The resulting lists of p-values from individual and set-wise testing are combined by means of meta analysis. We propose a new approach to connect microRNA-wise and gene set-wise information by means of p-value combination as often used in meta-analysis. In this context, we evaluate the usefulness of different approaches of gene set tests. In a simulation study we reveal that our combination approach is more powerful than microRNA-wise testing alone. Furthermore, we show that combining microRNA-wise results with 'competitive' gene set tests maintains a pre-specified false discovery rate. In contrast, a combination with 'self-contained' gene set tests can harm the false discovery rate, particularly when gene sets are not disjunct.     

Computational identification of microRNA targets

Recent experiments have shown that the genomes of organisms such as worm, fly, human, and mouse encode hundreds of microRNA genes. Many of these microRNAs are thought to regulate the translational expression of other genes by binding to partially complementary sites in messenger RNAs. Phenotypic and expression analysis suggests an important role of microRNAs during development. Therefore, it is of fundamental importance to identify microRNA targets. However, no experimental or computational high-throughput method for target site identification in animals has been published yet. Our main result is a new computational method that is designed to identify microRNA target sites. This method recovers with high specificity known microRNA target sites that have previously been defined experimentally. Based on these results, we present a simple model for the mechanism of microRNA target site recognition. Our model incorporates both kinetic and thermodynamic components of target recognition. When we applied our method to a set of 74 Drosophila melanogaster microRNAs, searching 3'UTR sequences of a predefined set of fly mRNAs for target sites which were evolutionary conserved between D. melanogaster and Drosophila pseudoobscura, we found that many key developmental body patterning genes such as hairy and fushi-tarazu are likely to be translationally regulated by microRNAs.     

流体剪切力对内皮细胞miR-21和miR-199a表达的影响

为探讨流体剪切力对内皮细胞micorRNAs表达的影响。采用旋转锥形圆盘剪切力系统对内皮细胞分别加载低(4dyn/cm2)、中(10 dyn/cm2)和高(15 dyn/cm2)3种不同梯度的剪切力作用24h。对照组未加载剪切力。采用高通量筛选芯片检测microRNAs表达变化,qRT-PCR验证,并进行生物信息学分析。与对照组比较,低剪切力组表达差异的microRNAs有33个(FC1.5或0.5倍,P0.05),其中28个上调,5个下调;中剪切力组表达差异的microRNAs有8个(FC1.5或0.5倍,P0.05),其中6个上调,2个下调;高剪切力组表达差异的microRNAs有31个(FC1.5或0.5倍,P0.05),其中25个上调,6个下调。miR-21在高剪切力组中上调最显著(FC=0.026),在低剪切力组中显著下调(FC=3.531)。miR-199a在低剪切力组中上调最显著(FC=0.075),在高剪切力组中显著下调(FC=3.031)。表达差异的microRNA的靶基因主要与内皮细胞的力学信号转导、细胞跨膜迁移、钙离子信号通路、细胞内吞作用等相关。流体剪切力可诱导内皮细胞miR-21和miR-199a表达发生改变。     

拟南芥中缺铁反应性microRNAs的鉴定

microRNA是一种非编码蛋白质的小分子RNA,参与了植物生长发育及环境胁迫响应的调控,主要通过对靶基因的负调控去影响生物学过程.基于前人对拟南芥全基因组microRNAs及其靶基因的预测,我们找到了靶向15个缺铁响应基因的22个microRNAs(miR158a、miR164c、miR172a、miR1887、miR2111ab、miR3933、miR395ade、miR414、miR828、miR831、miR837-3P、miR837-5P、miR854abcd、miR857、miR861-5P、miR864-5P).对这些microRNAs的启动子进行分析,发现分别有17、10和4个microRNAs启动子中包含缺铁响应元件IDE1、生长素响应元件和乙烯响应元件.进一步通过Poly(T)adaptor RT-PCR方法对这22个microRNAs在缺铁条件下的表达变化做了检测,结果显示,除miR158a和miR837-5P外的20个microRNAs在缺铁条件下的表达变化都有显著差异,且具有时间依赖性.这20个microRNAs可作为缺铁响应的候选microRNAs.     

Refining microRNA target predictions: Sorting the wheat from the chaff

microRNAs are short RNAs that reduce gene expression by binding to their targets. The accurate prediction of microRNA targets is essential to understanding the function of microRNAs. Computational predictions indicate that all human genes may be regulated by microRNAs, with each microRNA possibly targeting thousands of genes. Here we discuss computational methods for identifying mammalian microRNA targets and refining them for further experimental validation. We describe microRNA target prediction resources and procedures and how they integrate with various types of experimental techniques that aim to validate them or further explore their function. We also provide a list of target prediction databases and explain how these are curated.     

microRNA的调控和被调控

microRNA是一大类长度约22 nt的非编码RNA,可与靶基因的3′-UTR区部分或完全配对结合,进而通过降低靶mRNA的稳定性或抑制翻译而下调目的基因的表达. microRNA不仅参与细胞的增殖、分化、死亡等正常生理过程,而且还与包括癌症在内的诸多病理过程密切相关.microRNA通常位于编码基因的内含子区,主要由RNA聚合酶Ⅱ催化而转录为初始microRNA,接着经过一系列的核内、胞浆内酶切步骤而组装成有功能的RNA诱导的沉默复合体.本文将在简要介绍microRNA生物合成和调控功能的基础上,重点综述microRNA被调控的研究进展,主要包括表观遗传学水平、转录水平、转录后水平和降解的调控.近年来的研究,逐步丰富甚至推翻了以往对microRNA的认识,体现了microRNA生物学的复杂性.可以预见,随着研究的深入,microRNA将在疾病的早期防治中发挥越来越重要的作用.