Predicting Good RNAi Sites
RNA interference, the inhibition of gene expression by small double-stranded RNA, has recently emerged as a powerful strategy to dissect gene function and develop therapeutic interventions. At the technical level, one of the challenges has been to identify the specific RNA sites that are targetable.
“There are many prediction algorithms that were developed to do this, but none of them work very well,” says Kevin M. Weeks, professor of chemistry at the University of North Carolina at Chapel Hill.
To map RNA structures and predict sites that are most available for interactions, including sites that can be successfully targeted for therapeutic interventions, investigators in Dr. Weeks’ lab pioneered several years ago a technology known as SHAPE (selective 2´-hydroxyl acylation analyzed by primer extension).
“We focused on the naive idea that one of the main challenges in predicting good RNA interference sites is identifying the parts of the HIV genome that are free to interact with the RNAi machinery. The simple idea was that conformationally flexible regions might provide particularly good targets,” explains Dr. Weeks.
These sites are mostly those that do not have internal structures, and do not interact with themselves, whereas places where RNA folds back on itself are more hidden and less likely to represent optimal targets. SHAPE takes advantage of the fact that the nucleophilic reactivity of the ribose 2´-hydroxyl group is very sensitive to the nucleotide conformation and is modulated by its flexibility in the RNA backbone, and provides a powerful tool that allows RNA structure and dynamics to be examined under a broad range of biological environments.
“The key part of this work is that not only were we able to make good predictions, perhaps the best to date, but our calculations suggested that only approximately 2% of the HIV genome is a good target for the RNAi machinery, and we were able to identify a significant part of these targets,” says Dr. Weeks.
This approach, which can be used not only for additional pathogens, but also in other instances that require specific interactions with a target RNA, represents a promising strategy at the interface between RNA biology and therapeutics.
New Tool for Cancer Research
“For years, we identified splicing changes inside cancer cells, but it was debated whether they have causative roles or whether they are the consequence of disease-related deregulation,” says Michael C. Ryan, Ph.D., president and bioinformatics specialist at In Silico Solutions.
The general interest in alternative splicing emerged from the concept that, instead of mapping specific genes to a single protein, multiple different protein products can be formed, in a spatial and temporal manner, a process that required the “one gene, one polypeptide” concept to be revisited. Alternative splicing is one strategy to expand the proteome diversity and it has additional roles, such as protein quality control.
At the interface between alternative splicing and cancer research, an area of increasing interest has recently focused on a set of biological programs that is active and establishes specific splicing patterns during distinct stages of growth and development, but is turned off later on, in adult cells. “Some cancer cells appear to be able to find ways to turn these programs back on, and reactivate embryonic versions of the genes,” says Dr. Ryan.
A few years ago, alternative splicing was mostly studied by using microarrays. “While this provided interesting insights, next-generation sequencing currently offers a resolution that is an order of magnitude better,” explains Dr. Ryan.
To provide investigators with a better platform to examine and interpret alternative splicing patterns from RNA-Seq reads, Dr. Ryan and colleagues developed SpliceSeq, a free resource that is powered to capture changes during alternative splicing and explore their functional consequences.
“With SpliceSeq, one can map the reads to each individual exon or splice, so instead of examining the read for each gene, we can individually look at each element of a gene,” says Dr. Ryan. The platform opens the additional possibility to identify splicing patterns across multiple samples, perform comparative analyses, or group samples based on specific criteria, increasing the power of the analysis.
“Moreover, we can translate the reads into protein sequence prediction and subsequently identify the portion of the protein that is being impacted by alternative splicing,” explains Dr. Ryan.
Dr. Ryan and colleagues are currently comparing alternative splicing patterns across The Cancer Genome Atlas (TCGA) data, one of the biggest RNA-Seq repositories. “For the first time, SpliceSeq provides the opportunity to visualize changes at an unprecedented resolution,” explains Dr. Ryan.
Discoveries linked to the biology of RNA shaped seminal biomedical advances. One of the most intriguing aspects of RNA is its ability to fulfill diverse cellular functions, which include informational, structural, catalytic, and regulatory roles.
Many advances in this field, in addition to helping overturn old concepts and opening new perspectives, point toward a much more complex picture, one that unveils the dynamic nature of the scientific inquiry, which Carl Sagan so vividly captured in words: “There is much that science doesn’t understand, many mysteries still to be resolved. We are constantly stumbling on surprises.”