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Highly Efficient Generation of Germline Mutations Using CRISPR/Cas9 in the Speckled Wood Butterfly . | LitMetric

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Article Abstract

To date, the use of CRISPR/Cas9 technology in ecological-model species for validating genotype to phenotype connections has focused primarily on visual phenotypes using G mutations, coupled with analyses of resulting mosaic phenotypes. However, studies of physiological phenotypes necessitate germline mutations in order to assess non-visible phenotypic effects, and thus, dedicated efforts to develop efficient germline mutations in ecological model species are needed. Here, we applied the CRISPR/Cas9 technology to an ecological model species, the speckled wood butterfly (). We targeted , which is required for the production of black melanin, as loss of function (LOF) mutations are not lethal and easy to phenotype, affording efficient assessment of G and germline mutations. To explore what factors may affect the efficiency of transformation, we employed four alternative treatments, including variation in sgRNAs and their concentrations. Color changes in the head capsule of first larval instars, as well as adult wing color, were used as indicators of successful knockouts. Individuals with wings that were at least 50% transformed were mated, with their G1 offspring assessed for the presence of germline mutations. Our CRISPR/Cas9 technique was highly efficient at generating LOF mutations in . Across all treatments, nearly 80% of adults exhibited mosaic LOF phenotypes, of which nearly 30% appeared to have 100% LOF phenotypes. Crosses between adults exhibiting at least 50% LOF phenotypes resulted in fully transformed offspring, revealing a high incidence of germline LOF mutations in . We provide a detailed protocol on how to obtain high germline LOF mutation efficiency in order to advance the study of genotype-phenotype connections for non-visible physiological traits across natural populations of this and other model ecological species.

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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC12206953PMC
http://dx.doi.org/10.1002/ece3.71624DOI Listing

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