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

摘要：“开发一种高通量方法来验证体内microRNA生物合成” microRNA（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，并在结合后触发翻译抑制， 去腺苷化和靶mRNA的衰变。考虑到每一种miRNA都有可能调节数百种 每种细胞类型中都有几十种miRNAs表达，miRNAs是其中的主要基因之一。 在动物、植物和病毒中转录后水平的基因表达调节剂。因此， miRNA补体对于理解基因调控和mRNA周转是必不可少的。这个目标 该项目旨在开发一种用于miRNA发现和体内实验验证的高通量方法。 目前的miRNA发现方法严重依赖于提供适合小RNA的测序证据， 生物信息学预测的发夹状结构。问题是样本的可用性或 组织中的特定细胞类型可能是有限的，并且阻碍了可以用DNA备份的miRNA的数量。 排序支持。在这些情况下，当前的解决方案是更深地测序，从而增加总体成本。对 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， 验证它们加工成中间种类和成熟miRNA可以确认它们的身份。 最近的一项管理工作发现，在miRBase中超过7,000种后生动物的miRNA中， 数据库中，只有1，175个满足作为miRNAs所需的特征，直接拒绝了3，470个作为假阳性 缺乏足够的测序证据来识别另外2,105个miRNAs。因此，有一个未满足的 技术上需要开发一种miRNA验证方法，以增加获得miRNA候选物的机会 即使是从有限的样品，并且允许对其处理进行严格的多路实验验证。 在一个跨学科团队的协同作用下， 通过Cifuentes博士，PI）和生物信息学（Moxon博士，co-I），我们将开发miRAGe（miRNA分析 全基因组），一种整合miRNA预测及其实验验证的高通量方法， vivo. miRAGe背后的基本概念是使用活细胞作为“有机计算机”来分析miRNA 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， 测试它们在注射的斑马鱼胚胎中的处理过程。小RNA测序将确定哪些候选人是 通过检测它们的~22个核苷酸的成熟产物和它们的定型中间体来检测真正的microRNA。 总的来说，该项目的成果将使科学界获得高质量， 实验验证的miRNA数据来自任何生物体，基因组数据可用，从而提高未来 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.