A Molecular Grammar for Guide RNAs (gRNAs) with Engineered Secondary Structures - CRISPR effectors like Cas9 and Cas12a have emerged as powerful tools in biomedical research for their ability to introduce targeted mutations in living cells and, consequentially, for this ability they hold significant therapeutic potential for treating genetic disorders—despite also carrying significant clinical risk that they may introduce ‘off- target’ or unintended mutations. While the power of CRISPR effectors lies in the fact that the sequences they recognize and target are complementary to a modular, ‘programmable’ segment of their RNA cofactors (their ‘guide RNAs’ or gRNAs), their mutational activity can be triggered at nucleotide sequences with imperfect complementarity to their gRNAs as well, unpredictably. Obviously, the possibility of uncontrolled mutation raises red flags for both patients and clinicians and so far, CRISPR gene therapies have been focused on highly specialized genetic situations. Further improvements to CRISPR specificity are necessary, not only to mitigate clinical risk, but also to drive new applications of CRISPR—for example, if single nucleotide variants (SNVs) could be reliably discriminated, it would allow for allele-specific gene editing of autosomal dominant disorders, where often we would need to discriminate between small sequence variations between the ‘healthy’ and ‘disease’ alleles but which current CRISPR technologies cannot consistently do. We recently demonstrated the feasibility of an approach that is capable of improving CRISPR effector specificity by orders-of-magnitude, and in such a way that it can be synergistically applied to many of the other previously-developed techniques to improve specificity further. By adding extra nucleotides to the gRNA (x-gRNA) and designing the extended sequence to form ‘hairpin’ secondary structures with the DNA-targeting segment of the gRNA (hairpin-gRNAs or hp-gRNAs) that destabilize interactions with off-targets, we could generate x/hp-gRNAs that significantly limited off-target activity while maintaining on-target mutational activity in CRISPR effector variants derived from four different organisms and one engineered derivative. The long-term goal is therefore to understand the rules for designing extended sequences in x-gRNAs that would result in ultra-specificity for divergent CRISPR effectors at any CRISPR-targetable site. To achieve that goal, in this R21 we will perform an exhaustive screen of randomized x-gRNA libraries targeting different clinically-relevant sites and identify what common sequence and/or secondary-structure features of those x-gRNAs drive significant increases in specificity. While the riskiness of this proposal is that there may not be “universal design rules”, per se, for all x/hp-gRNA designs and targets, this work will nevertheless provide a practical (design-free) platform for researchers to empirically generate ultra-specific x-gRNAs for any target of interest for any CRISPR effector. The likely reward is that synergistic use of x-gRNAs (with engineered CRISPR effectors) has the potential to effectively abrogate the risk of unintended mutation in CRISPR applications, and that combining our high-throughput approach with machine- learning would allow new computational tools for anyone to produce, de novo, ultra- or allele-specific x-gRNAs.
