Genetics of dark matter transcription in yeast

描述（由申请人提供）：来自编码未知功能元件的基因座的转录在人类基因组和许多模型系统中广泛存在。基因组生物学的一个关键挑战是确定哪些“暗物质”转录本在功能上是相关的。这种对功能性RNA的搜索部分是由RNA作为治疗人类疾病的靶标和作为治疗剂本身的潜力所激发的。研究者提出，以酵母为模型，在高通量规模上开发推断未注释转录本功能的方法。此前，研究人员开创了遗传多样性个体之间mRNA表达差异的遗传分析。在实验基因组学，软件开发和分子遗传学的专业知识的基础上，研究人员现在提出了在酵母中开发未注释的推定非编码RNA的相关策略。主要目标是利用已知基因和未注释的转录本的共调控来推断后者的功能。该项目将绘制酵母菌株之间的DNA差异，导致RNA水平的变化-注释和未注释。遗传作图软件将鉴定主调节子中的多态性，每个多态性影响多个下游靶的反式表达。在这样的调节子中，功能基因组分析将发现已知基因之间的共同途径成员，从而推断未注释的转录本也在相同途径中起作用。绘图软件还将识别顺式调控元件中的多态性，每个多态性影响附近编码的转录物的水平;这将允许发现新RNA的启动子和其他顺式作用调控区。分子方法将为预测的单个RNA的功能和调节提供实验证实。这些RNA的发现，以及使它们成为可能的软件工具，将成为后生动物未来工作的跳板。 公共卫生相关性：用小分子药物治疗人类疾病需要费力地测试化合物的特异性和效力，并且很大程度上限于靶向具有小配体结合位点的蛋白质。与RNA相互作用的合成寡核苷酸疗法正在成为一种潜在的革命性替代方案，但基于RNA的药物领域仍处于起步阶段。该提案旨在开发以高通量规模推断新型RNA功能的方法和工具，最终拓宽治疗靶点的前景。

DESCRIPTION (provided by applicant): Transcription from loci encoding no known functional elements is widespread in the human genome, and in many model systems. A key challenge in genome biology is to determine which such "dark matter" transcripts are functionally relevant. This search for functional RNAs is motivated in part by the potential of RNAs as targets for treatment of human disease, and as therapeutic agents themselves. The investigator proposes to develop methods to infer function of un-annotated transcripts on a high-throughput scale, using yeast as a model. Previously, the investigator pioneered the genetic analysis of mRNA expression differences between genetically diverse individuals. On the basis of this demonstrated expertise with experimental genomics, software development, and molecular genetics, the investigator now proposes to develop a related strategy for un-annotated, putative noncoding RNAs in yeast. The principal goal is to harness the co-regulation of known genes and un-annotated transcripts to infer function of the latter. The project will map DNA differences between yeast strains that cause variation in levels of RNAs-both annotated and un-annotated. Software for genetic mapping will identify polymorphisms in master regulators, each of which affects the expression of multiple downstream targets in trans. In such a regulon, functional genomic analysis will find common pathway membership among known genes, leading to the inference that un- annotated transcripts also function in the same pathway. Mapping software will also identify polymorphisms in cis-regulatory elements, each of which affects levels of a transcript encoded nearby; this will allow the discovery of promoters and other cis-acting regulatory regions for novel RNAs. Molecular methods will provide experimental confirmation of the predicted function and regulation of individual RNAs. Discoveries of these RNAs, and the software tools that enable them, will serve as a springboard for future work in metazoans. PUBLIC HEALTH RELEVANCE: The treatment of human disease with small molecule drugs requires laborious testing of compounds for specificity and potency, and is largely restricted to the targeting of proteins with small ligand binding sites. Synthetic oligonucleotide therapeutics that interact with RNAs are emerging as a potentially revolutionary alternative, but the field of RNA-based drugs is still in its infancy. This proposal aims to develop approaches and tools that infer function of novel RNAs on a high-throughput scale, ultimately widening the landscape of targets for therapeutics.