Combinatorial microRNA regulation of cardiac transcription factors
Combinatorial microRNA regulation of cardiac transcription factors
批准号:
8392248
负责人:
RICHARD H. GOODMAN
金额:
$18.33万
依托单位国家:
美国
项目类别:
财政年份:
2011
资助国家:
美国
项目状态:
已结题
起止时间:
2011-12-01 至 2013-11-30
关键词:
3&apos Untranslated RegionsAffectAlgorithmsBindingBioinformaticsBiologicalBiological AssayCardiacCardiac MyocytesCardiovascular DiseasesCellsCharacteristicsComplexDataDominant-Negative MutationElementsGoalsHeartHeart DiseasesHumanIndividualMediatingMessenger RNAMethodsMicroRNAsModelingMonitorMutateMutationPathway interactionsProteinsRNA-Induced Silencing ComplexRegulationReporterRepressionRoleSeminalSignal TransductionSmall RNASpecificitySystemTestingTissuesUntranslated Regionsbasecombinatorialdesigninhibitor/antagonistinsightnovelnovel strategiespreventratiometricsensortranscription factor
中文摘要
描述(由申请人提供):单个心脏细胞可以表达数百种microrna,每个microrna都可能有数百个靶标,这一事实提出了这样一个复杂的调节模式如何可能实现特异性的问题。我们假设这是通过单个3' utr同时与多个microrna相互作用的能力发生的,这样两个(或更多)microrna的同时结合是生物学上有意义的调控所必需的。利用我们开发的一种生物化学鉴定microRNA-mRNA相互作用的方法,我们发现miR-1和miR-133a与心脏转录因子Hand2的3'UTR同时关联。这可能是迄今为止在任何两个microrna与单个靶标同时结合的系统中最好的例子。我们假设miR-1和miR-133a与Hand2 3'UTR的结合是相互依赖的,因此这两个microrna必须与其识别元件(MREs)相关联才能实现有效的抑制。这种机制会增加信号的复杂性,产生靶向特异性,并限制单个microRNA有效mRNA靶点的数量。这种情况可能是其他microRNA靶点的特征,我们将使用我们实验室开发的一种新检测方法来识别受类似双重调节模式影响的其他心脏mrna。这些研究可以回答microRNA信号传导的一个重要问题——大量microRNA和预测的靶标如何实现特异性。为了实现我们的目标,我们将确定miR-1和miR-133a如何协同调节Hand2的表达。我们的数据表明miR-1和miR-133a的结合是相互依赖的,这在以前从未被描述过。我们将通过利用一组新的双向比例传感器来阐明这一发现的功能含义。为了阐明miR-1和miR-133a结合相互依赖的机制,我们将RNA诱导沉默复合体(RISC)的核心成分Ago2靶向突变的Hand2 miR-1或miR-133a MREs,以确定募集Ago2和相关蛋白是否可以挽救MRE突变的影响。我们还将确定miR-1和miR-133a的协调调节是否与其他心脏mrna共享。众所周知,用于预测microRNA靶标的生物信息学算法在识别真实相互作用的能力上是不精确的,遗漏了许多相互作用,并错误地预测了其他相互作用。我们开发了一种识别这些靶标的新方法,该方法采用显性负RISC成分在降解之前捕获microRNA-mRNA中间体。我们将使用这种方法来鉴定与miR-1和miR-133a相关的其他心脏mrna,并测试microRNA的结合和功能是否同样相互依赖。了解microrna的组合如何影响靶标的表达对于开发有效的基于microrna的疗法至关重要。
英文摘要
DESCRIPTION (provided by applicant): The fact that individual cardiac cells can express hundreds of microRNAs, each with potentially hundreds of targets, raises the question of how such a complex mode of regulation can possibly achieve specificity. We hypothesize that this occurs through the ability of individual 3'UTRs to interact with multiple microRNAs simultaneously, such that concurrent binding of two (or more) microRNAs is required for biologically meaningful regulation. Using a method that we developed to identify microRNA-mRNA interactions biochemically, we showed that miR-1 and miR-133a associate simultaneously with the 3'UTR of the cardiac transcription factor, Hand2. This is probably the best example to date in any system of concurrent binding by two microRNAs to a single target. We hypothesize that binding of miR-1 and miR-133a to the Hand2 3'UTR is mutually interdependent, such that both microRNAs must associate with their recognition elements (MREs) to achieve efficient repression. Such a mechanism would increase signaling complexity, generating specificity of targeting and constraining the number of effective mRNA targets for an individual microRNA. This scenario may be characteristic of other microRNA targets, and we will use a new assay developed in our lab to identify additional cardiac mRNAs that are subject to a similar dual mode of regulation. These studies could answer a seminal question in microRNA signaling-how the multitude of microRNAs and predicted targets can achieve specificity. To accomplish our goals, we will determine how miR-1 and miR-133a cooperate to regulate Hand2 expression. Our data indicates that binding of miR-1 and miR-133a is mutually interdependent, which has never before been described. We will elucidate the functional implications of this finding by utilizing a set of novel bidirectional ratiometric sensors. To elucidate the mechanism underlying the interdependency of miR-1 and miR-133a binding, we will target Ago2, a core component of the RNA induced silencing complex (RISC), to the mutated Hand2 miR-1 or miR-133a MREs to determine whether recruitment of Ago2 and associated proteins can rescue effects of the MRE mutations. We will also determine whether the coordinate regulation by miR-1 and miR-133a is shared by other cardiac mRNAs. Bioinformatic algorithms designed to predict microRNA targets are notoriously imprecise in their ability to identify authentic interactions, missing many interactions and falsely predicting others. We developed a novel approach for identifying these targets that employs a dominant negative RISC component to trap microRNA-mRNA intermediates prior to degradation. We will use this approach to identify other cardiac mRNAs that associate with both miR-1 and miR-133a and test whether microRNA binding and function are similarly interdependent. Understanding how combinations of microRNAs affect expression of targets is essential for developing effective microRNA-based therapies.
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