Mutant allele formation and inheritance during Cas9/guide RNA-mediated gene drive in a population modification mosquito strain for human malaria control

在利用 Cas9/引导 RNA 介导的基因驱动技术对蚊群进行改造以控制人类疟疾的过程中,突变等位基因的形成和遗传

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Abstract

Gene-drive systems are under development for the population modification of anopheline vectors of human malaria parasites. The key to their success is the fixation of genes in target mosquito populations that encode molecules preventing parasite transmission. High-efficiency Cas9/guide RNA (gRNA)-based gene-drive systems can facilitate this objective. A potential challenge to these systems is the presence of naturally-occurring or drive system-induced sequence polymorphisms in the genomic target site that could impede Cas9/gRNA-mediated cleavage and negatively impact gene-drive dynamics and fixation. Careful choice of the target site can mitigate the impact of natural variation, and here we analyze drive system-mediated, target-site mutagenesis in the outcross and testcross progeny of an Anopheles gambiae strain homo- and hemizygous for the TP13-based gene-drive system. The resulting data allow for estimation of the rates at which drive-system activity generates mutant target-site alleles in the germline and the impact of inherited paternal- and maternal-effect mutations. Functional and nonfunctional mutant alleles are recovered from the germlines at average rates per target gene/generation of 0.08% for paternal and 0.33% for maternal testcross lineages, with an overall average rate of 0.21%. Clustering effects amplify the potential inheritance frequencies of the mutant alleles. Mutations originating in the germlines represent 47% of the total inherited in testcross progeny, with the balance coming from mutant alleles generated by paternal and maternal effects inherited through the respective parental lineages. This approach allows the estimation of potential cleavage-resistant allele formation and inheritance for this drive system in this species and provides empirically derived values to inform more realistic data-driven gene-drive modeling.

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