Frameworks for large-scale RNA structure profiling in transcriptomes and disease

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  • March 19, 2019
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  • Busan, Steven
    • Affiliation: College of Arts and Sciences, Department of Chemistry
Abstract
  • In addition to their role as intermediaries on the route to protein synthesis, RNA molecules have long been known to base-pair into complex structures that serve specific functions. Some structured RNAs play pathogenic roles, especially in viral illnesses and repeat-expansion disorders, and disease-associated RNA structures are potential therapeutic targets. SHAPE is a well-established chemical probing strategy to interrogate RNA flexibility and obtain high-quality structure models. The recent development of an unbiased experimental approach that allows SHAPE to characterize populations of diverse RNAs using massively parallel sequencing presented a challenging data analysis problem. In this work, I apply SHAPE to study the relevance of huntingtin mRNA structure to Huntington's disease and discover that a classical CAG hairpin is likely absent or short in healthy-length transcripts. The formation of this hairpin correlates with increasing repeat length, which is a predictor of disease severity. I develop a fully-automated data analysis pipeline allowing for the extension of the SHAPE strategy to larger scales using mutational profiling (MaP), an approach that was applied to identify highly-structured elements within an HIV-1 genome. I further provide a pilot analysis of a bacterial transcriptome MaP dataset obtained in a single experiment, demonstrate the nucleotide accuracy of MaP within this large sample, and apply alignment clustering to identify conserved motifs at the genomic scale. Together, these three projects highlight the power of SHAPE to identify specific RNA structures related to human disease and the value of robust experimental design and careful analysis in large-scale sequencing studies of RNA structure.
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  • In Copyright
Advisor
  • Weeks, Kevin
  • Pielak, Gary J.
  • Brustad, Eric
  • Laederach, Alain
  • Jarstfer, Michael
Degree
  • Doctor of Philosophy
Degree granting institution
  • University of North Carolina at Chapel Hill Graduate School
Graduation year
  • 2015
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  • Chapel Hill, NC
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  • There are no restrictions to this item.
Date uploaded
  • June 23, 2015

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