Genome-Wide Probing of RNA Structures In Vitro Using Nucleases and Deep Sequencing

RNA structures in alternative splicing and back-splicing

Alternative splicing greatly expands the transcriptomic and proteomic diversities related to physiological and developmental processes in higher eukaryotes. Splicing of long noncoding RNAs, and back- and trans- splicing further expanded the regulatory repertoire of alternative splicing. RNA structures were shown to play an important role in regulating alternative splicing and back-splicing. Application of novel sequencing technologies made it possible to identify genome-wide RNA structures and interaction networks, which might provide new insights into RNA splicing regulation in vitro to in vivo. The emerging transcription-folding-splicing paradigm is changing our understanding of RNA alternative splicing regulation. Here, we review the insights into the roles and mechanisms of RNA structures in alternative splicing and back-splicing, as well as how disruption of these structures affects alternative splicing and then leads to human diseases. This article is categorized under: RNA Processing > Splicing Regulation/Alternative Splicing RNA Structure and Dynamics > Influence of RNA Structure in Biological Systems.

The Untranslated Regions of mRNAs in Cancer

The 5' and 3' untranslated regions (UTRs) regulate crucial aspects of post-transcriptional gene regulation that are necessary for the maintenance of cellular homeostasis. When these processes go awry through mutation or misexpression of certain regulatory elements, the subsequent deregulation of oncogenic gene expression can drive or enhance cancer pathogenesis. Although the number of known cancer-related mutations in UTR regulatory elements has recently increased markedly as a result of advances in whole-genome sequencing, little is known about how the majority of these genetic aberrations contribute functionally to disease. In this review we explore the regulatory functions of UTRs, how they are co-opted in cancer, new technologies to interrogate cancerous UTRs, and potential therapeutic opportunities stemming from these regions.

Deep sequencing in the management of hepatitis virus infections

The hepatitis viruses represent a major public health problem worldwide. Procedures for characterization of the genomic composition of their populations, accurate diagnosis, identification of multiple infections, and information on inhibitor-escape mutants for treatment decisions are needed. Deep sequencing methodologies are extremely useful for these viruses since they replicate as complex and dynamic quasispecies swarms whose complexity and mutant composition are biologically relevant traits. Population complexity is a major challenge for disease prevention and control, but also an opportunity to distinguish among related but phenotypically distinct variants that might anticipate disease progression and treatment outcome. Detailed characterization of mutant spectra should permit choosing better treatment options, given the increasing number of new antiviral inhibitors available. In the present review we briefly summarize our experience on the use of deep sequencing for the management of hepatitis virus infections, particularly for hepatitis B and C viruses, and outline some possible new applications of deep sequencing for these important human pathogens.

The impact of RNA secondary structure on read start locations on the Illumina sequencing platform

High-throughput sequencing is subject to sequence dependent bias, which must be accounted for if researchers are to make precise measurements and draw accurate conclusions from their data. A widely studied source of bias in sequencing is the GC content bias, in which levels of GC content in a genomic region effect the number of reads produced during sequencing. Although some research has been performed on methods to correct for GC bias, there has been little effort to understand the underlying mechanism. The availability of sequencing protocols that target the specific location of structure in nucleic acid molecules enables us to investigate the underlying molecular origin of observed GC bias in sequencing. By applying a parallel analysis of RNA structure (PARS) protocol to bacterial genomes of varying GC content, we are able to observe the relationship between local RNA secondary structure and sequencing outcome, and to establish RNA secondary structure as the significant contributing factor to observed GC bias.