Discovery of structural RNAs involved in human health and disease

发现与人类健康和疾病有关的结构RNA

基本信息

批准号：
10704745
负责人：
Elena Rivas
金额：
$ 34.86万
依托单位：
HARVARD UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2022
资助国家：
美国
起止时间：
2022-09-15 至 2026-07-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10704745
关键词：
3-Dimensional Adopted Algorithms Amino Acid Sequence Attention Bacteria Base Pairing Bioinformatics Biological Biological Process Biology Cell Extracts Cell physiology Cells Characteristics Chromatography Complex Computing Methodologies Coupled Detection Disease Enzymes Escherichia coli Evolution Exhibits Genetic Epistasis Genetic Transcription Genome Genomics Health Human Human Biology Human Genome Knowledge Left Life MALAT1 gene Machine Learning Markov Chains Mediating Methodology Methods MicroRNAs Mutation Names Peptides Phylogenetic Analysis Phylogeny Play Positioning Attribute Primates Proteins Publications Publishing RNA RNA Splicing Recording of previous events Reporter Genes Research Ribosomal RNA Role Saccharomyces cerevisiae Sequence Alignment Signal Transduction Small Nucleolar RNA Structure System Technology Telomerase Testing Transcript Translations Triplet Multiple Birth Untranslated RNA Untranslated Regions Variant Vertebrates Virus Work causal variant computerized tools deep learning expectation fungus genome wide screen genome-wide improved insight learning strategy mammalian genome model organism next generation novel preservation programs protein structure prediction targeted treatment therapeutic development three dimensional structure tool vertebrate genome virtual

项目摘要

Many fundamental cellular functions depend on a variety of RNA structures conserved through evolution, and other functional RNA structures are expected to be discovered. A signature of a conserved RNA structure is found in alignments where paired positions display correlated substitutions (covariation) that preserve the base pair. This evolutionary signal can be used both to predict RNA structure and to identify new conserved RNAs. Recent publications and preliminary results have made three important advances: A statistical covariation test that identiﬁes signiﬁcant covariation over background covariation due to phylogeny. This test, implemented in a method called R-scape (RNA Structural Covariation Above Phylogenetic Expectation), provides information and control over the rate of false positive predictions. A power of covariation calculation, recently published, that identiﬁes “negative” pairs with power (variation) but insigniﬁcant covariation, unlikely to form RNA base pairs. A new cascading folding algorithm, named CaCoFold (Cascade covariation/variation Constrained Folding) also recently published, that combines all positive and negative evolutionary information into complex structures including all types of pseudoknots and triplets. In human, the efﬁcacy of these advances has been tested by ac- curately predicting the structures of the human non-coding RNAs MALAT1 and telomerase RNA, and by inferring that the non-coding RNAs HOTAIR and XIST do not have a conserved structure. These three advances give us a competitive advantage to perform unbiased genome-wide screens for con- served structural RNAs in vertebrates with accurate 3D structure prediction. Previous vertebrate screens for structural RNAs have been hindered by thousand of false positive predictions. In contrast, our new covariation statistical test allows for controlling the rate of false positives. R-scape has already been used to ﬁnd struc- tural RNAs in bacteria and viruses. Our recent eukaryotic pilot screen in fungi has identiﬁed 17 novel structural RNAs. We hypothesize that many structural RNAs with implications for human health and disease are still to be discovered, and that we now have the tools to ﬁnd and characterize these RNAs. This proposal has three speciﬁc aims that will advance the study of structural RNA biology, and the discov- ery of novel biological mechanisms involving RNA structures. The ﬁrst aim proposes systematic genome-wide searches to ﬁnd novel conserved vertebrate RNA structures in human. The second aim proposes to combine revolutionary 3D structure prediction methods in machine learning with the signals used by CaCoFold into a state of the art RNA folding method for the accurate prediction of 3D RNA structures. The third aim introduces a method to identify RNA structures in ultra conserved vertebrate UTRs where there is no covariation signal, and our current method lacks power. We expect our work will unveil primate-speciﬁc novel regulatory mecha- nisms. Novel human RNA structures found to have causal variants associated with disease will be prioritized for experimental veriﬁcation.

许多基本的细胞功能依赖于进化过程中保守的各种 RNA 结构，并且其他功能性 RNA 结构有望被发现。在配对位置显示保留碱基的相关替换（共变）的比对中发现该进化信号可用于预测 RNA 结构和识别新的保守 RNA。最近的出版物和初步结果取得了三项重要进展：统计协变识别由于系统发育而导致的背景协变的显着共变的测试。在一种称为 R-scape（RNA 结构协变高于系统发育期望）的方法中，提供了信息以及对误报预测率的控制，最近发表的协变计算的力量，识别具有功率（变异）但不显着的共变的“负”对，不太可能形成 RNA 碱基对。一种新的级联折叠算法，命名为CaCoFold（Cascade covariation/variation Constrained Folding）最近也发表了，它将所有积极和消极的进化信息结合成复杂的结构包括所有类型的假结和三联体，这些进步的功效已经在人类中得到了检验。准确预测人类非编码 RNA MALAT1 和端粒酶 RNA 的结构，并通过推断非编码 RNA HOTAIR 和 XIST 不具有保守结构。这三项进步为我们提供了竞争优势，可以进行无偏见的全基因组筛选为脊椎动物提供结构 RNA，并进行准确的 3D 结构预测。相比之下，我们的新协变受到了数千个假阳性预测的阻碍。统计测试可以控制误报率，R-scape 已被用于寻找结构。我们最近在真菌中进行的真核试验筛选发现了 17 种新的结构。我们探索了许多对人类健康和疾病具有影响的结构 RNA。发现了，并且我们现在拥有寻找和表征这些 RNA 的工具。该提案具有三个具体目标，将推进结构 RNA 生物学的研究，并发现涉及RNA结构的一系列新颖的生物机制的第一个目标是提出系统性的全基因组。第二个目标是寻找人类中新的保守脊椎动物 RNA 结构。机器学习中革命性的 3D 结构预测方法，将 CaCoFold 使用的信号转化为用于准确预测 3D RNA 结构的最先进的 RNA 折叠方法第三个目标介绍了一种方法。鉴定没有共变信号的超保守脊椎动物 UTR 中 RNA 结构的方法，我们目前的方法缺乏效力，我们预计我们的工作将揭示灵长类动物特异性的新型调控机制。发现具有与疾病相关的因果变异的新型人类 RNA 结构将被优先考虑。实验验证。