Data from: Highly efficient generation of germline mutations using CRISPR/Cas9 in the speckled wood butterfly Pararge aegeria
Data files
Jun 18, 2025 version files 15.04 KB
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Adult_weight_G0_G1.txt
478 B
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Adults_Yellow_treatments.txt
687 B
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Larvae_Yellow_treatments.txt
1.15 KB
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Life_history_G0_G1.txt
787 B
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README.md
4.27 KB
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Survival_Adult.txt
1.38 KB
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Survival_Egg.txt
5.29 KB
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Survival_G0_G1_only_observed_matings.txt
309 B
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Yellow_treatments_100_.txt
687 B
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 G0 mutations coupled with analyses of resulting mosaic phenotypes. However, studies of physiological phenotypes necessitate germline mutations in order to assess non-visible phenotypic effects, thus dedicated efforts to developing 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 (Pararge aegeria). We targeted yellow-y, which is required for the production of black melanin, as yellow-y loss of function (LOF) mutations are not lethal and easy to phenotype, affording efficient assessment of F0 and germline mutations. To explore what factors may affect efficiency of transformation, we employed four alternative treatments, including variation in sgRNAs and their concentrations. Color changes in the head capsule of first larval instar 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 F1 offspring assessed for the presence of germline mutations. Our CRISPR/Cas9 technique was highly efficient at generating LOF mutations in yellow-y. 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 high incidence of germline LOF mutations in yellow-y. 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.
https://doi.org/10.5061/dryad.9ghx3fftg
Description of the data and file structure
Files and variables
File: Larvae_Yellow_treatments.txt
Description: Binary data for testing if the four CRISPR/Cas9 treatments differ in the frequency of individuals appearing as knock-out (excl. controls) based on the color of the head capsule of the first instar larva.
Variables
- Construct: the treatment the individual was subjected to, y1+3, y1+s4, y1, y2.
- Yellow: whether the individuals head capsule appeared dark brown like the wildtype (0) or light brown (1).
File: Adults_Yellow_treatments.txt
Description: Binary data for testing if the four CRISPR/Cas9 treatments differ in the frequency of individuals appearing as knock-out (excl. controls) based on the wing background color of the adult butterflies.
Variables
- Construct: the treatment the individual was subjected to, y1+3, y1+s4, y1, y2.
- Yellow: whether the individuals background wing color appeared dark brown like the wildtype (0) or mosaic or 100% transformed (1).
File: Yellow_treatments_100_.txt
Description: Binary data for testing if the four CRISPR/Cas9 treatments differ in the frequency of adult individuals appearing as fully knock-out.
Variables
- Construct: the treatment the individual was subjected to, y1+3, y1+s4, y1, y2.
- Yellow: whether the individuals background wing color appeared wildtype or mosaic in colors (0) or appeared 100% transformed (1).
File: Survival_Adult.txt
Description: Binary data for testing if the treatments differ in egg survival.
Variables
- Treatment: the treatment the individual was subjected to, y1+3, y1+s4, y1, y2, C, W.
- Survival_Adult: whether the individual survived until eclosion (1) or not (0).
File: Survival_Egg.txt
Description:
Variables
- Treatment: the treatment the individual was subjected to, y1+3, y1+s4, y1, y2, C, W.
- Survival_Egg: whether the individual survived until hatching (1) or not (0).
File: Adult_weight_G0_G1.txt
Description: Data for assessing if knock-out of the yellow-y gene has a pleiotropic effect on adult weight.
Variables
- Generation: the generation the individual belonged to, either G0 (control) or G1 (germline).
- Adult weight: the weight of the adult individual in g
- Sex: the sex of the individual (M: male, F: female)
File: Life_history_G0_G1.txt
Description: Data for assessing if knock-out of the yellow-y gene has pleiotropic effects on other life history traits.
Variables
- Generation: the generation the individual belonged to, either G0 (control) or G1 (germline).
- LDT: the larval development time of the individual in days
- PDT: the pupal development time of the individual in days
- Pupal weight: the weight of the individual’s pupa in g
- Sex: the sex of the individual (M: male, F: female)
File: Survival_G0_G1_only_observed_matings.txt
Description: Binary data for testing if knock-out of the yellow-y gene affects survival to adult eclosion.
Variables
- Generation: the generation the individual belonged to, either G0 (control) or G1 (germline).
- Survival: whether the individual survived until eclosion (1) or not (0).
Code/software
The analyses can be performed in R (version 4.1.2) using the car and emmeans packages.
G0_G1.R : This R script analyzes whether the knock-out of the yellow-y gene affects survival and other life history traits by comparing control (G0) and germline (G1) generations using logistic regression and ANOVA.
Treatment_F0.R: This R script evaluates the effect and efficiency of CRISPR/Cas9-mediated yellow-y gene knock-out in F0 individuals by comparing treated vs. control groups and different constructs using chi-square tests, logistic regression, ANOVA, and post hoc comparisons.
Survival.R: This R script analyzes whether different treatments affect survival at the egg and adult stages using logistic regression, ANOVA, and post hoc comparisons.