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Collaborative Research: Multimodal RNA structural motifs in alphavirus genomes: discovery and validations

合作研究：甲病毒基因组中的多模式 RNA 结构基序：发现和验证

基本信息

批准号：
10226177
负责人：
Christine E Heitsch
金额：
$ 33.7万
依托单位：
GEORGIA INSTITUTE OF TECHNOLOGY
依托单位国家：
美国
项目类别：
财政年份：
2017
资助国家：
美国
起止时间：
2017-08-01 至 2024-07-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10226177
关键词：
3-Dimensional Address Adopted Alphavirus Animals Automobile Driving Base Pairing Base Sequence Biochemistry Biological Capsid Chemicals Code Collaborations Combinatorics Communities Complex Computer Analysis Computing Methodologies Coupled Cryoelectron Microscopy Disease Disease Outbreaks Elements Evolution Family Gene Expression Genome Genomics Human Immune Evasion Individual Investigation Magnetic Resonance Imaging Maps Mathematics Messenger RNA Methodology Methods Mining Molecular Conformation Molecular Structure Organism Plant Viruses Prevention Proteins RNA RNA Folding RNA Sequences RNA Viruses Research Resources Ribosomal RNA Role Sampling Signal Transduction Structure Testing Theoretical model Time Transfer RNA Untranslated RNA Validation Viral Viral Genome Viral Physiology Virus X-Ray Crystallography algebraic topology algorithm development base chikungunya combinatorial experimental study frontier insight interest mathematical analysis mathematical model multimodality next generation novel pathogen synergism theories

项目摘要

The most well-studied RNA molecular structures, notably tRNA and rRNA, exist as a single dominant conformation. However, a growing number of small non-coding RNA sequences are known to function by switching between multiple stable configurations. It is expected that such multi modal structural motifs punctuate the ensemble of low-energy structures for an RNA viral genome like Chikungunya, regulating the viral lifecycle. Characterizing these small overlapping sets of stable base pairs, embedded in lengthy sequences with high structural diversity, is essential to understanding how critical structural signals encode the functionality of these important pathogens. This collaboration leverages complementary strengths of previous results --- mining competing signals from the structural ensemble (profiling) and next generation chemical footprinting (SHAPE-MaP) --- to tackle the challenge of multi modal motif discovery in a test set of three alphavirus genomes. This first aim will be achieved by developing the necessary characterizations of profiling landscapes and of SHAPE-MaP signatures to identify target regions with multiple native conformations. These separate results will be validated in individual sequences by the combination of SHAPE-directed profiling, following experimental confirmation of the current prediction methodology. The second aim will demonstrate evolutionary support for these new motifs, first across the three test sequences and then the entire alphaviral family, through a new application of computational algebraic topology. Persistent homology and simplicial complexes will be used to analyze evolution across the different scales at which biological information is encoded in RNA viral genomes, ranging from genomic sequence to vertebrate host. This will be followed by chemical probing confirmation for three additional alphavirus sequences. This project will extend the frontiers of RNA folding by integrating new mathematical models and analyses based on combinatorics and algebraic topology with recent advances in the biochemistry of chemical footprinting for the purposes of identifying significant motifs with multimodal structure in lengthy RNA viral genomes. The results of this study, a set of novel secondary structure motifs in alphavirus genomes which are ideal candidates for further investigation as important functional elements, will be a key resource for RNA virologists. Furthermore, the proposed theoretical and algorithmic developments are generally applicable to all RNA viruses, and hence of significant utility and interest to the scientific community.

研究最充分的RNA分子结构，特别是tRNA和rRNA，以单一优势构象存在。然而，已知越来越多的小的非编码RNA序列通过在多个稳定构型之间切换来发挥功能。预计这种多模态结构图案会打断 RNA病毒基因组的低能量结构，如基孔肯雅病毒，调节病毒的生命周期。表征这些小的重叠的稳定碱基对集合，嵌入在具有高结构多样性的长序列中，对于理解关键结构信号如何编码这些重要病原体的功能至关重要。这项合作利用了以前结果的互补优势-从结构集合（分析）和下一代化学足迹（SHAPE-MaP）中挖掘竞争信号-以应对在三个甲病毒基因组的测试集中发现多模式基序的挑战。这第一个目标将通过开发必要的表征的轮廓景观和形状-地图签名，以确定目标区域与多种天然构象。这些单独的结果将在单个序列中通过结合SHAPE定向分析进行验证，然后对当前的预测方法进行实验确认。第二个目标将证明这些新的基序的进化支持，首先跨越三个测试序列，然后是整个甲病毒家族，通过计算代数拓扑的新应用。持久的同源性和单纯复合物将用于分析生物信息在RNA病毒基因组中编码的不同尺度的进化，从基因组序列到脊椎动物宿主。随后将对另外三个甲病毒序列进行化学探测确认。该项目将通过整合新的数学模型和基于组合学和代数拓扑学的分析以及化学足迹生物化学的最新进展来扩展RNA折叠的前沿，以确定长RNA病毒基因组中具有多模态结构的重要基序。这项研究的结果是甲病毒基因组中一组新型二级结构基序，它们是进一步研究重要功能元件的理想候选者，将成为RNA病毒学家的关键资源。而且所提出的理论和算法发展通常适用于所有RNA病毒，因此对科学界具有重要的实用性和兴趣。