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Targeting Genes, More Accurately

by Aakriti Jain

The CRISPR-Cas9 DNA editing tool is arguably one of the most revolutionary tools in biotechnology, the applications of which range from biofuels to medicine. Until now, a caveat of the tool was the time and effort spent in finding the perfect guide RNA to target selected genes effectively.

Infographic on how the CRISPR-Cas9 tool works. Courtesy of the Innovative Genomics Institute.

CRISPR (also known as Clustered Regularly Interspaced Short Palindromic Repeats) and CRISPR-associated (Cas) genes are an essential part of adaptive immunity in an assortment of bacteria and archaea. These organisms use CRISPR-Cas systems to fight off invading genetic material. The CRISPR repeats were actually discovered in the 1980s in E. coli, and in their function was confirmed in 2007 byBarrangou et al, in Streptococcus thermophilus, which acquired resistance against bacteriophage by incorporating part of the genome of the infecting virus into its CRISPR locus.

It wasn’t until 2012 that the potential of this system was realized by Doudna and Charpentier. The system required only two components: the Cas9 and a synthetic single guide RNA (sgRNA). The sgRNA programs the Cas9 to attach to DNA, separate the DNA helix, find the correct section of the DNA. The matching sequence needs to be found next to a PAM (or protospacer adjacent motif) sequence in order for the Cas9 protein to lock down and make a double stranded cut in the DNA sequence.

As with all gene editing tools, the CRISPR-Cas9 system isn’t perfect. A parameter that is often used to assess genome-editing tools is targeting efficiency, or the percentage of desired mutation that is achieved. In general, the targeting efficiency of Cas9 is much better than that of other editing tools, such as ZFNs and TALENs.

Even though Cas9 is able to precisely carry out genome editing functions, more than one sgRNA can be a possible match for any given gene target. This means that scientists have to spend a considerable amount of time conducting preliminary troubleshooting experiments in order to select the best guide RNA for the given task.

In order to eliminate some of the trial-and-error process that comes with selecting guide RNAs, a team from the Church lab developed a predictive software tool that can help find the best sgRNA to direct Cas9 towards the gene targets.

The “library on library” approach used to create a software algorithm that can predict the best way to target any specific gene. Courtesy of Wyss Institute at Harvard University.

The software is publicly available for anyone to use and works by hierarchically ranking how effectively any given sgRNA would target the gene in question based on experimental data from human genomes (unlike other comparable algorithms, which work solely based on first principles).

We started off by asking ourselves, is there something in the guide RNA sequence that could suggest one would work better than others? — Raj Chari, PhD, lead author on study.

Church’s group conducted a high-throughput study of the activity between 1500 gene targets and their complementary guide RNAs in order to find patterns in the sgRNA sequence that would indicate the effectiveness of binding. By doing this, they were able to find a unique sequence from each gene target, and create a library of all the targets, inserting them into cultured human cells. To test which guide RNAs would be most effective, they inserted the complementary RNAs with Cas9 and conducted genome extraction and sequencing in order to determine which guide RNA was the best match for each target.

They then used this experimental data to develop the novel algorithm that ranked and scored the most effective guide RNAs for targeting essentially any human gene, even ones that weren’t experimented on in order to develop the software. They were able to do so by identifying unique features in guide RNA sequences that indicated how well they would work for a given gene target.

Such software can help scientists working with the CRISPR-Cas9 tool to reduce time spent on trial-and-error experiments by narrowing down possible sgRNA sequences and focus on finding creative solutions to larger problems in order to accelerate research in the expansive fields and applications genome editing.

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