A Molecular Grammar for Guide RNAs (gRNAs) with Engineered Secondary Structures

像Cas9和Cas12a这样的CRISPR效应器因其能力而成为生物医学研究中的强大工具 在活细胞中引入有针对性的突变，因此，对于这种能力，它们具有重要的治疗作用 治疗遗传性疾病的潜力--尽管它们可能会带来重大的临床风险-- 目标突变或意外突变。虽然CRISPR效应器的能力在于它们的序列 识别和靶向是对其RNA辅助因子(其 ‘引导RNAs’或gRNAs)，它们的突变活性可在核苷酸序列不完善时触发 它们的gRNA也具有互补性，这是不可预测的。显然，不受控制的突变的可能性增加了 对患者和临床医生来说都是危险信号，到目前为止，CRISPR基因疗法一直高度关注 特殊的遗传情况。有必要进一步改进CRISPR的专用性，不仅是为了减轻 临床风险，但也推动CRISPR的新应用-例如，如果单核苷酸变体(SNV) 可以被可靠地区分，它将允许对常染色体显性遗传病进行等位基因特异性基因编辑， 我们常常需要区分“健康的”和“健康的”之间的小序列差异 “疾病”等位基因，但目前的CRISPR技术无法始终如一地做到这一点。我们最近演示了 能够将CRISPR效应器的特异性提高数量级的方法的可行性，以及 以这样一种方式，它可以协同应用于许多其他先前开发的技术，以 进一步提高特异性。通过在gRNA(x-gRNA)中添加额外的核苷酸并设计扩展的 与gRNA的DNA靶向片段(发夹-gRNAs或 Hp-gRNAs)破坏与靶外相互作用的稳定性，我们可以产生显著限制 在CRISPR效应器变体中保持靶外活性的同时保持靶上突变活性 不同的生物体和一种工程衍生品。因此，长期目标是理解以下规则 在x-gRNA中设计扩展序列，从而对发散的CRISPR效应器产生超特异性 在任何CRISPR目标站点。为了实现这一目标，在本R21中，我们将详尽地筛选 针对不同临床相关部位的随机x-gRNA文库并确定哪些共同序列 和/或这些x-gRNA的二级结构特征显著提高了特异性。而当 这一提议的风险在于，本身可能不存在适用于所有x/hp-gRNA设计的“通用设计规则”。 和目标，这项工作仍然将为研究人员提供一个实用的(无需设计的)平台 为任何CRISPR效应器的任何感兴趣的目标生成超特定的x-gRNA。可能的回报是 协同使用x-gRNA(与工程CRISPR效应器)有可能有效地消除这种风险 CRISPR应用中的意外突变，并将我们的高通量方法与机器相结合- 学习将使任何人都可以使用新的计算工具来产生超特异或等位基因特异的x-gRNA。

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.