Genetic association studies identify the genotypes that correlate with specific phenotypes. A significant portion of the Single Nucleotide Polymorphisms (SNPs) that associate with human disease phenotypes map outside of the protein coding regions of genes. In these cases, the precise molecular mechanism of the disease etiology is not immediately apparent. This proposal focuses specifically on SNPs that map to non-coding and UnTranslated Regions (UTRs) of genes. If these SNPs alter the structure of the RNA, they are classified as a riboSNitch. We will experimentally validate eight novel, computationally predicted riboSNitches associated with the human diseases amyotrophic lateral sclerosis, breast and colorectal cancer, dyskeratosis, Hirschsprung's disease, lipase deficiency, microcephalic dwarfism, and schizophrenia. Our work will leverage significant advances in the throughput and accuracy of chemical structure probing techniques in combination with next generation sequencing. Furthermore, these techniques now enable us to probe RNA structure in vivo allowing us to further understand how the cellular environment affects RNA folding and the function of riboSNitches. We will also perform quantitative luciferase reporter assays and leverage Tet-off inducible systems to study the functional consequences of validated riboSNitches on translation and RNA stability to establish disease causality. Together, our findings will establish SNP-induced RNA structure change in multiple new human diseases and broaden understanding of RNA structure in shaping human phenotype.

Public Health Relevance

This project aims to understand the effects of disease-associated mutations on the structure and function of RNA (RiboNucleic Acid). Mutations are copied from DNA into RNA and, in some cases, they cause the RNA to misfold, resulting in aberrant regulation and disease. We will develop and experimentally validate computer algorithms that are highly predictive of the effects of mutations on RNA structure in living human cells, to identify novel genetic mechanisms of disease caused by RNA structure change.

Agency
National Institute of Health (NIH)
Institute
National Institute of General Medical Sciences (NIGMS)
Type
Research Project (R01)
Project #
5R01GM101237-07
Application #
9773104
Study Section
Macromolecular Structure and Function D Study Section (MSFD)
Program Officer
Krasnewich, Donna M
Project Start
2012-05-01
Project End
2021-08-31
Budget Start
2019-09-01
Budget End
2020-08-31
Support Year
7
Fiscal Year
2019
Total Cost
Indirect Cost
Name
University of North Carolina Chapel Hill
Department
Biology
Type
Schools of Arts and Sciences
DUNS #
608195277
City
Chapel Hill
State
NC
Country
United States
Zip Code
27599
Kutchko, Katrina M; Madden, Emily A; Morrison, Clayton et al. (2018) Structural divergence creates new functional features in alphavirus genomes. Nucleic Acids Res 46:3657-3670
Lackey, Lela; Coria, Aaztli; Woods, Chanin et al. (2018) Allele-specific SHAPE-MaP assessment of the effects of somatic variation and protein binding on mRNA structure. RNA 24:513-528
Woods, Chanin Tolson; Laederach, Alain (2017) Classification of RNA structure change by 'gazing' at experimental data. Bioinformatics 33:1647-1655
Ball, Christopher B; Solem, Amanda C; Meganck, Rita M et al. (2017) Impact of RNA structure on ZFP36L2 interaction with luteinizing hormone receptor mRNA. RNA 23:1209-1223
Kutchko, Katrina M; Laederach, Alain (2017) Transcending the prediction paradigm: novel applications of SHAPE to RNA function and evolution. Wiley Interdiscip Rev RNA 8:
Corley, Meredith; Solem, Amanda; Phillips, Gabriela et al. (2017) An RNA structure-mediated, posttranscriptional model of human ?-1-antitrypsin expression. Proc Natl Acad Sci U S A 114:E10244-E10253
Gamache, Eric R; Doh, Jung H; Ritz, Justin et al. (2017) Structure-Function Model for Kissing Loop Interactions That Initiate Dimerization of Ty1 RNA. Viruses 9:
Woods, Chanin T; Lackey, Lela; Williams, Benfeard et al. (2017) Comparative Visualization of the RNA Suboptimal Conformational Ensemble In Vivo. Biophys J 113:290-301
Mucaki, Eliseos J; Caminsky, Natasha G; Perri, Ami M et al. (2016) A unified analytic framework for prioritization of non-coding variants of uncertain significance in heritable breast and ovarian cancer. BMC Med Genomics 9:19
Schulmeyer, Kayley H; Diaz, Manisha R; Bair, Thomas B et al. (2016) Primary and Secondary Sequence Structure Requirements for Recognition and Discrimination of Target RNAs by Pseudomonas aeruginosa RsmA and RsmF. J Bacteriol 198:2458-69

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