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UCSF Researcher Highlights the Use of shRNA Libraries to Study Genetic Interactions


Amid the continued advancement of biomedical research, it is becoming increasingly apparent that polytherapies — treatment regimens based on multiple drugs that address multiple aspects of a disorder — can offer superior efficacy to monotherapies.

However, identifying the genes involved in complex diseases and, more importantly, understanding how their interactions affect the conditions remains a challenge to identifying appropriate treatment combinations, according to Michael McManus, a University of California, San Francisco, investigator and founder of the W.M. Keck Center for Non-coding RNAs.

Scientists have been interested in studying genetic interactions for decades, but have lacked the tools required to effectively do so, he told Gene Silencing News last week on the sidelines of the GTC Nucleic Acid Summit in San Francisco. But with the development of advanced genomics technologies, researchers now have the resources to move beyond single-gene studies.

Key among these technologies are shRNA libraries, and USCF has developed a genome-wide shRNA collection containing around 55,000 hairpins targeting roughly 2,100 human genes, which translates to about 25 shRNAs per gene — one of the largest available at any institution.

McManus noted during a presentation at the event that the breadth of UCSF’s shRNA resource is an important aspect to their usefulness, helping to mitigate the false negatives and false positives that occur with lower-diversity libraries, and he and his colleagues at USCF previously reported on a method to create and monitor such high-coverage libraries.

In a 2009 Nature Methods paper, the team described the generation of a pilot library encoding 22,000 shRNAs targeting roughly 600 genes, including almost all of the known human CD antigens, optimizing conditions for PCR amplification, cloning, and propagation. The library was then analyzed by deep sequencing.

McManus’ team was able to identify multiple active shRNAs specific for each gene, a “critical” improvement over existing methodologies that allows a “rigorous statistical evaluation of whether a gene is a true hit” and which is a direct result of the expanded number of shRNAs per gene in their library, they wrote in the paper.

With UCSF’s wide-ranging shRNA collection at their disposal, McManus and collaborators from UCSF and other institutes more recently developed a strategy to create genetic interaction maps, or GIs, which feature pairwise measurements of how strongly the presence of one gene influences the mutation phenotype of another.

As they reported in Cell earlier this year, not only can GIs uncover functional relationships between genes, “the pattern of GIs of a gene provides an information-rich description of its phenotype, which can be used to detect functional similarities between genes and reveal pathways without prior assumptions about cellular functions.”

Yet systematically quantifying GIs can be a troublesome undertaking, in part because “high-precision phenotypic measurements” are needed to accurately determine GIs, which is done by quantifying the deviation of an observed double-mutant phenotype from the one expected from two individual mutants, they noted in their paper. In addition, GIs are rare and therefore a high-throughput approach is needed to generate large, high-density GI maps.

“At the same time, the large number of possible pairwise interactions in the human genome makes it necessary to focus on a subset of genes with common biological functions to create a sufficiently dense GI map to reveal meaningful insights,” they added.

Aiming to address these hurdles, the researchers developed a two-step approach to creating the maps in mammalian cells. The first step is to conduct a genome-wide screen in order to identify genes functioning in a particular biological pathway and the shRNAs that effectively target them using ultracomplex shRNA libraries. Then, double-shRNA libraries are constructed to systematically measure GIs between hits.

In the work described in Cell, McManus and his colleagues used the method to create a GI map to study genetic modifiers of cellular susceptibility to the toxin ricin, not only recapitulating a number of known pathways but also making some unexpected discoveries including a “non-canonical role” for COPI, a previously uncharacterized protein complex affecting toxin clearance.

To McManus, the availability of advanced genomic tools such as highly diverse shRNA libraries marks a key step forward for getting at the heart of the multi-gene interactions that drive disease, which should lead to better and better treatment options for diseases such as cancer.

He also noted at the GTC meeting that while the UCSF community benefits from the availability of the school’s broad shRNA collection, enabling his and other labs there to conduct genome-wide experiments, most investigators have specific areas of focus and therefore don’t require access to such massive libraries.

And with his team’s protocols for shRNA library construction available in the literature, he told Gene Silencing News, it is relatively easy for a researcher to create their own high-coverage hairpin collections.