Developing a high-throughput method to validate microRNA biogenesis in vivo.

摘要：《建立一种高通量的体内验证microRNA生物发生的方法》 MicroRNAs(MiRNAs)是一类参与基因表达抑制的小分子调控RNA家族。他们 通过碱基配对识别它们的靶mRNA，并在结合时触发翻译抑制， 靶mRNAs去烯化和衰变。鉴于每个miRNA都有可能调节数百个 在基因中，每种细胞类型都表达几十个miRNAs，miRNAs是其中的主要分子之一 在动物、植物和病毒转录后水平的基因表达调节。因此，对 MiRNA互补对于理解基因调控和mRNA周转是必不可少的。这样做的目的是 该项目旨在开发一种高通量的方法，用于体内miRNA的发现和实验验证。 目前的miRNA发现方法在很大程度上依赖于提供适合于 一种发夹状的结构预示着生物信息学。问题是样品的可用性或样品的丰度 组织中的特定细胞类型可能受到限制，并阻碍可用于备份的miRNAs的数量 测序支持。在这些情况下，当前的解决方案是更深入地排序，增加总体成本。在……上面 另一方面，~22-NT的小RNA的存在并不能保证属于miRNA家族， 因为细胞RNA的随机降解也会产生这种大小的小RNA，而且只会进行繁琐的实验 对它们加工成中间物种和成熟miRNA的验证可以确认它们的身份。 最近的一项整理工作发现，在miRBase中的7000多个后生动物miRNA中，参考miRNA 数据库中，只有1,175个完成了作为miRNAs传递所需的特征，直接拒绝了3,470个假阳性 并且缺乏足够的测序证据来预测另外2105个miRNA。因此，有一个未满足的 需要开发一种miRNA验证方法，以提高对miRNA候选基因的访问 即使是有限的样品，这也允许对其处理进行严格的多路实验验证。 与一个结合了microRNA和斑马鱼专业知识的跨学科团队的协同作用 操控(Cifuentes博士，Pi)和生物信息学(Moxon博士，co-I)，我们将开发Mirage(miRNA分析 基因组范围)，一种集成miRNA预测和实验验证的高通量方法 活着。幻影背后的基本概念是将活细胞用作分析miRNA的“有机计算机” 正在处理。我们将使用大规模并行寡核苷酸池合成来制备miRNA候选前体和 在注射的斑马鱼胚胎中测试它们的处理过程。小RNA测序将决定哪些候选者是 通过检测他们的~22核苷酸成熟产物和他们的刻板印象中间体来获得真正的microRNA。 总体而言，该项目的成果将使科学界获得高质量的机会， 来自任何生物体的实验验证的miRNA数据，以及现有的基因组数据，从而增强了未来 转录后调控的横向研究。

ABSTRACT: “Developing a high-throughput method to validate microRNA biogenesis in vivo” microRNAs (miRNAs) are a family of small regulatory RNAs involved in repression of gene expression. They recognize their target mRNAs though base pairing and upon binding, trigger translational repression, deadenylation and decay of the target mRNAs. Given that each miRNA has the potential to regulate hundreds of genes, and several dozens of miRNAs are expressed in each cell type, miRNAs are one of the master regulators of gene expression at post-transcriptional level in animals, plants and virus. Thus, the elucidation of the miRNA complement is essential to understand gene regulation and mRNA turnover. The goal of this project is to develop a high-throughput method for miRNA discovery and experimental validation in vivo. Current miRNA discovery methods heavily rely on providing sequencing evidence of a small RNA that fits into a hairpin-like structure predicted bioinformatically. The problem is that sample availability or the abundance of a specific cell type in a tissue may be limited and hamper the number of miRNAs that can be backed-up with sequencing support. In these situations, the current solution is to sequence deeper, increasing overall costs. On the other hand, the presence of a small RNA of ~22-nt does not guarantee the belonging to the miRNA family, as random degradation of cellular RNAs also will produce small RNAs of this size and only tedious experimental validation of their processing into intermediate species and mature miRNA can confirm their identity. A recent curation work found that of the over ~7,000 metazoan miRNAs in miRBase, the reference miRNA database, only 1,175 fulfilled the features necessary to pass as miRNAs, directly rejected 3,470 as false positives and lacked enough sequencing evidence to call another 2,105 miRNAs. Therefore, there is an unmet technological need to develop a miRNA validation method that boosts the access to the miRNA candidates even from limited samples and that allows rigorous multiplexed experimental validation of their processing. With the synergy of an interdisciplinary team that combines expertise in microRNAs and zebrafish manipulation (Dr. Cifuentes, PI) and bioinformatics (Dr. Moxon, co-I), we will develop miRAGe (miRNA Analysis Genome wide), a high-throughput method to integrate miRNA prediction and their experimental validation in vivo. The fundamental concept behind miRAGe is the use of living cells as “organic computers” to analyze miRNA processing. We will use massively parallel oligo pool synthesis to prepare the miRNA candidate precursors and test their processing in injected zebrafish embryos. Small RNA sequencing will determine which candidates are true microRNA by detecting their ~22-nucleotide mature product and their stereotypic intermediates. Overall, the results from this project will transform the access of the scientific community to high quality, experimentally validated miRNA data from any organism with genomic data available, thus enhancing future transversal studies on post-transcriptional regulation.