Structural and functional consequences of disease SNPs on the transcriptome

疾病 SNP 对转录组的结构和功能影响

基本信息

批准号：
8273826
负责人：
Alain T Laederach
金额：
$ 27.36万
依托单位：
UNIV OF NORTH CAROLINA CHAPEL HILL
依托单位国家：
美国
项目类别：
财政年份：
2012
资助国家：
美国
起止时间：
2012-05-01 至 2016-04-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/8273826
关键词：
5&apos Untranslated Regions Accounting Adopted Affect Algorithms Base Pairing Binding Binding Proteins Binding Sites Biological Assay Blood capillaries Cataract Cells Cellular Structures Chemical Structure Computational algorithm Computer Simulation Data Data Reporting Detection Development Disease Elements Exons Ferritin Functional RNA Gene Expression Profile Gene Mutation Genes Genetic Genetic Transcription Genetic Variation Genome Genotype Haplotypes Human Human Genetics Human Genome Introns Iron Length Light Linkage Disequilibrium Maps Mediating Messenger RNA Modeling Molecular Molecular Conformation Mutation Nucleic Acid Regulatory Sequences Oryctolagus cuniculus Performance Phenotype Predictive Value Probability Process Protein Binding Publishing RB1 gene RNA RNA-Binding Proteins Regulation Regulatory Element Response Elements Reticulocytes Retinoblastoma Role Single Nucleotide Polymorphism Structure Syndrome System Techniques Testing Tetracyclines Thalassemia Transcript Translations Untranslated Regions Validation capillary disease phenotype functional restoration genetic linkage genome wide association study high throughput technology human disease improved in vivo interest locked nucleic acid mRNA Stability next generation novel

项目摘要

DESCRIPTION (provided by applicant): Large-scale genetic studies identify new associations between genotype and disease-phenotype (Benjamin et al. 2007; Lee et al. 2008; Mathew 2008; Glinskii et al. 2009). In many cases, and in particular when the associated genotype maps to a non-coding region of the genome, the genetic data alone does not reveal the molecular cause of the disease (Glinskii et al. 2009). Non-coding regions of the genome are in a majority of cases transcribed into RNA (Ribonucleic acid) (Weinstock 2007), and if a disease-associated mutation alters the structure of the transcript, this may have functional consequences (Halvorsen et al. 2010). We have recently identified disease-associated SNPs (Single Nucleotide Polymorphisms) in the regulatory regions of mRNA transcripts that significantly alter the folding of the transcript. Much like bacterial Riboswitches (Tucker and Breaker 2005), RiboSNitches adopt significantly altered conformations if a specific SNP is present (Halvorsen et al. 2010). Furthermore, we have shown that secondary mutations and binding of genotype specific locked nucleic acids (LNAs) can rescue the structure and regulatory function of the RNA. We hypothesize that specific haplotypes (combinations of SNPs in high linkage disequilibrium) will stabilize certain transcripts. We propose to use our predictive SNPfold (Halvorsen et al. 2010) algorithm (which models the ensemble of possible RNA conformations) to identify RNA structure-stabilizing haplotypes in the human genome and thus discover and experimentally validate novel posttranscriptional cellular regulatory mechanisms. We are fundamentally interested in understanding the structural consequences of common genetic variation on the function of the transcriptome. PUBLIC HEALTH RELEVANCE: Genetic mutations that cause human disease are encoded in our DNA (Deoxy riboNucleic Acid) but generally affect downstream molecular processes in our cells. This proposal aims to understand the effects of disease-associated mutations on the structure and function of RNA (RiboNucleic Acid). RNA is a genetic messenger molecule that is also a central component of the cell's regulatory machinery. Mutations are transcribed into RNA and in some cases will alter the structure of the RNA, causing it to misfold and this results in aberrant regulation and disease. We will develop and validate computer algorithms that are highly predictive of the effects of mutations on the structure of RNA to identify mutations that ar likely to disrupt its function.

描述（由申请人提供）：大规模的遗传研究确定了基因型与疾病 - 表型之间的新关联（Benjamin等，2007； Lee等，2008； Mathew 2008； Glinskii等，2009）。在许多情况下，特别是当相关的基因型映射到基因组的非编码区域时，仅遗传数据并不能揭示疾病的分子原因（Glinskii等，2009）。基因组的非编码区域在大多数转录为RNA中的病例（核糖核酸）（Weinstock 2007），如果与疾病相关的突变改变了转录本的结构，则这可能具有功能性后果（Halvorsen等，2010）。我们最近在mRNA转录本的调节区域中确定了与疾病相关的SNP（单核苷酸多态性），这些SNP会显着改变转录本的折叠。与细菌核糖开关一样（Tucker and Breaker 2005），如果存在特定的SNP，核能组织会采用显着改变的构象（Halvorsen等，2010）。此外，我们已经表明，基因型特异性核酸（LNA）的二次突变和结合可以挽救RNA的结构和调节功能。我们假设特定的单倍型（在高连接不平衡的SNP的组合）将稳定某些转录本。我们建议使用我们的预测性SNPFOLD（Halvorsen等，2010）算法（模拟可能的RNA构象的集合）来鉴定人类基因组中的RNA结构稳定的单倍型，从而发现并通过实验性地进行了新的细胞后细胞调节机制。我们从根本上有兴趣了解常见遗传变异对转录组功能的结构后果。公共卫生相关性：引起人类疾病的基因突变是在我们的DNA中编码的（脱氧核糖核酸），但通常会影响我们细胞中下游分子过程。该建议旨在了解与疾病相关的突变对RNA（核糖酸）结构和功能的影响。 RNA是一种遗传信使分子，也是细胞调节机制的核心组成部分。突变被转录为RNA，在某些情况下会改变RNA的结构，从而导致其错误折叠，这会导致异常调节和疾病。我们将开发和验证计算机算法，这些算法高度预测突变对RNA结构的影响，以鉴定可能破坏其功能的突变。