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Genome editing retraces the evolution of toxin resistance in the monarch butterfly

Nature volume 574, pages 409–412 (2019)Cite this article

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Abstract

Identifying the genetic mechanisms of adaptation requires the elucidation of links between the evolution of DNA sequence, phenotype, and fitness1. Convergent evolution can be used as a guide to identify candidate mutations that underlie adaptive traits2,3,4, and new genome editing technology is facilitating functional validation of these mutations in whole organisms1,5. We combined these approaches to study a classic case of convergence in insects from six orders, including the monarch butterfly (Danaus plexippus), that have independently evolved to colonize plants that produce cardiac glycoside toxins6,7,8,9,10,11. Many of these insects evolved parallel amino acid substitutions in the α-subunit (ATPα) of the sodium pump (Na+/K+-ATPase)7,8,9,10,11, the physiological target of cardiac glycosides12. Here we describe mutational paths involving three repeatedly changing amino acid sites (111, 119 and 122) in ATPα that are associated with cardiac glycoside specialization13,14. We then performed CRISPR–Cas9 base editing on the native Atpα gene in Drosophila melanogaster flies and retraced the mutational path taken across the monarch lineage11,15. We show in vivo, in vitro and in silico that the path conferred resistance and target-site insensitivity to cardiac glycosides16, culminating in triple mutant ‘monarch flies’ that were as insensitive to cardiac glycosides as monarch butterflies. ‘Monarch flies’ retained small amounts of cardiac glycosides through metamorphosis, a trait that has been optimized in monarch butterflies to deter predators17,18,19. The order in which the substitutions evolved was explained by amelioration of antagonistic pleiotropy through epistasis13,14,20,21,22. Our study illuminates how the monarch butterfly evolved resistance to a class of plant toxins, eventually becoming unpalatable, and changing the nature of species interactions within ecological communities2,6,7,8,9,10,11,15,17,18,19.

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Fig. 1: Mutational paths in ATPα associated with insect specialization on cardiac glycoside-producing plants are constrained.
Fig. 2: Drosophila flies with edited genomes retrace the mutational path of ATPα in the monarch butterfly lineage and show increased survival when fed cardiac glycosides.
Fig. 3: The mutational path of ATPα in the monarch butterfly lineage sequentially increases TSI to ouabain and is shaped by epistasis.

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Data availability

The data supporting the findings of this study are available within the paper and its Supplementary Information.

Code availability

The code to compute RMO (reproducibility of mutational order) scores in the presence of unordered mutations and correlated pathways can be accessed in Github (https://github.com/gaguerra/ModifiedRMO). The set of R scripts implements the RMO score, first proposed by Toprak and co-workers14, with the new additions of accounting for non-independent mutational pathways (in the presence of shared ancestry) and partially unresolved mutational pathways.

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Acknowledgements

We thank V. Wagschal, who helped with the construction and testing of in vitro cell lines; E. Toprak, who provided MATLAB code; T. O’Connor, who aided in the sequestration analyses; the Essig Museum of Entomology for photographs of milkweed butterfly specimens; M. Fa and K. O’Connor-Giles for advice on the development of the fly lines; and E. LaPlante for assistance with feeding assays. D. Bachtrog, K. Mooney, P. Moorjani, M. Nachman, R. Nielsen, P. Sudmant, R. Tarvin and B. Walsh provided feedback that improved the manuscript. Access to the HPC resources of Aix-Marseille Université was supported by the Equip@Meso (ANR-10-EQPX-29-01) project of the Investissements d’Avenir supervised by the Agence Nationale de la Recherche. This project was supported by grants from the Gordon and Betty Moore Foundation (Life Sciences Research Foundation Postdoctoral Fellowship Grant GBMF2550.06 to S.C.G.), the German Research Foundation (DFG, grant Do527/5-1 to S.D.), the Agence National de la Recherche (grant no. BioHSFS ANR-15-CE11-0007 to F.S. and F.R.), the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program (grant agreement no. 772257 to F.S. and F.R.), the National Geographic Society (grant 9097-12 to N.K.W.), the National Science Foundation (grant DEB-1256758 to N.K.W. and IOS-1907491 to A.A.A.), the John Templeton Foundation (grant ID 41855 to A.A.A., S.D., and N.K.W.), and the National Institute of General Medical Sciences of the National Institutes of Health (award no. R35GM119816 to N.K.W.).

Author information

Author notes

  1. These authors contributed equally: Marianthi Karageorgi, Simon C. Groen.

Authors and Affiliations

  1. Department of Integrative Biology, University of California, Berkeley, Berkeley, CA, USA

    Marianthi Karageorgi, Simon C. Groen, Julianne N. Pelaez, Kirsten I. Verster, Jessica M. Aguilar, Susan L. Bernstein, Teruyuki Matsunaga, Michael Astourian & Noah K. Whiteman

  2. Department of Biology, Center for Genomics and Systems Biology, New York University, New York, NY, USA

    Simon C. Groen

  3. LAI, U1067 Aix-Marseille Université, Inserm, CNRS, Marseille, France

    Fidan Sumbul & Felix Rico

  4. Department of Ecology and Evolutionary Biology, Cornell University, Ithaca, NY, USA

    Amy P. Hastings & Anurag A. Agrawal

  5. Department of Statistics, University of California, Berkeley, Berkeley, CA, USA

    Geno Guerra

  6. Molecular Evolutionary Biology, Zoological Institute, Biocenter Grindel, Universität Hamburg, Hamburg, Germany

    Susanne Dobler

  7. Department of Entomology, Cornell University, Ithaca, NY, USA

    Anurag A. Agrawal

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  1. Marianthi Karageorgi
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Contributions

M.K. co-designed and implemented the overall strategy for the creation of the knock-in fly lines, designed and implemented the bioassays, the RT–qPCR experiments and the RMO analysis, performed statistical analyses and co-wrote the manuscript. S.C.G. designed and implemented the overall strategy for the creation of the knock-in fly lines, prepared the sequence data and metadata for the phylogenetic analyses, co-designed all other experiments, and co-wrote the manuscript. F.S. performed the structural modelling and docking site analyses. J.N.P. performed the phylogenetic, ancestral state and co-evolutionary analyses. K.I.V. conducted crosses, genotyping, and feeding experiments, and co-designed the qPCR experiments. J.M.A. and S.L.B. conducted crosses and genotyping, and feeding and sequestration experiments. A.P.H. performed the in vitro physiological experiments and sequestration analyses. T.M. conducted feeding experiments M.A. performed the RMO analysis with M.K., and conducted genotyping and feeding experiments. G.G. completed the RMO and ouabain dietary survival analyses. F.R. supervised the structural modelling and docking site analyses. S.D. oversaw and interpreted in vitro cell line analyses, helped to design the overall project and co-wrote the manuscript. A.A.A. helped to design the overall project, oversaw the in vitro physiological and sequestration experiments, and co-wrote the manuscript. N.K.W. led the overall collaboration, the project design and its integration, creation of fly lines and statistical analyses, and co-wrote the manuscript.

Corresponding author

Correspondence to Noah K. Whiteman.

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The authors declare no competing interests.

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Peer review information Nature thanks Joseph W. Thornton and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Extended data figures and tables

Extended Data Fig. 1 Substitutions at ATPα amino acid residues 111, 119 and 122 are directly or indirectly associated with insect specialization on plants that produce cardiac glycosides.

a, The number of occurrences of each substitution across the 21 lineages in which specialization evolved independently. b, TraitRateProp analysis of the H1–H2 loop of ATPα across insects shows amino acid residues that are strongly associated with feeding on cardiac glycoside-producing plants and toxin sequestration. Bayes factor values in the top histogram indicate per-site associations between feeding and sequence rate evolution, values in the bottom histogram indicate per-site associations between sequestration and sequence rate evolution. Values over 10 were considered different (asterisks). For information on the species included in the analysis please see Supplementary Text. Colours in the multi-sequence alignment represent individual amino acids. c, BGM shows the correlated evolution of amino acid sites within the H1–H2 loop of ATPα. The table shows the marginal posterior probabilities (PP) between amino acid interactions, where the PP exceeds a default cut-off of 0.5. The residue interactions are depicted graphically, with amino acid sites represented by the nodes and the PP associated with a given epistatic or co-evolutionary interaction indicated by the values at the arrows. Nodes circled in orange indicate amino acid sites that are the focus of experiments in this study. Sites 111 and 122 are very strongly associated with feeding and sequestering, and site 119 co-evolves with site 111.

Extended Data Fig. 2 A two-step genome editing approach using CRISPR–Cas9 and HDR to generate knock-in Atpα lines of Drosophila melanogaster.

In the first step, the region encoding the H1–H2 extracellular domain was replaced with a 3×P3::GFP marker through CRISPR–Cas9-mediated HDR. This generated a common parent line with the deletion allele AtpαDeletion (GFP+). In the second step, the 3×P3::GFP marker was replaced with each of the synonymous and non-synonymous point mutation alleles through an additional round of CRISPR–Cas9-mediated HDR to generate the knock-in lines. The crossing schemes to establish the deletion line and the knock-in lines following the first and second rounds of HDR, respectively, are also shown. See also Methods and Supplementary Tables 4–7 for further details on the genome engineering strategy and crosses behind the establishment of the knock-in lines.

Extended Data Fig. 3 Point mutations have some effect on adult emergence, but do not lead to major changes in baseline Atpα expression or Na+/K+-ATPase activity.

a, Percentages of emerging adults of the knock-in and control lines on standard Drosophila medium (n = 7–8 vials, 100 eggs per vial, mean ± s.e.m.). Survival of the knock-in lines and control lines QAN (engineered control) and QAN* (w1118 wild type) was compared using one-way ANOVA (P < 0.001). Survival differed between QAN* and some of the knock-in lines, but not between the engineered control line QAN and any of the knock-in lines except VSN (post hoc Tukey’s tests (letters)). b, Atpα expression was not different among the engineered Drosophila knock-in lines or w1118 wild-type flies (QAN*). Atpα transcript level differences were assayed by qPCR. Expression was assayed in three biological replicates (symbols represent the mean ± s.e.m.), with two technical replicates per biological replicate (averaged for each biological replicate), of five- to six-day-old females as fold change standardized against rpl32 expression in QAN* flies. The expression fold change between genotypes was compared using one-way ANOVA (P = 0.3197). c, None of the sequential Atpα genotypes found along the monarch lineage affected base-line levels of pump activity in a sodium pump enzymatic assay using extracts of fly heads (one-way ANOVA, P = 0.1377; symbols represent the mean ± s.e.m. of 3–7 biological replicates). Further information on experimental design and statistical test results is in the Source Data. ns, not significant.

Extended Data Fig. 4 Drosophila flies with edited genomes show increased larval–adult survival when fed cardiac glycosides.

These panels accompany Fig. 2c. Larval–adult survival when reared on diets with a range of ouabain concentrations was different between monarch lineage knock-in lines relative to control lines (QAN, engineered control; QAN*, w1118 wild type). Symbols represent the mean ± s.e.m. of 3–6 biological replicates (50 larvae per replicate). Curves were fit through a univariate logistic regression (effect of ouabain concentration on survival), and the difference in survivorship trajectories between each pair of fly lines (genotypes) was evaluated by performing an LRT to assess the significance of the inclusion of an interaction term between genotype and ouabain concentration in the logistic regression for a pair of lines (**P < 0.01, ***P < 0.001). Further information on experiment design and statistical test results is in the Source Data.

Source data

Extended Data Fig. 5 Drosophila flies with edited genomes show increased adult survival when fed cardiac glycosides.

These panels accompany Fig. 2d. Adult survival when reared on diets with a range of ouabain concentrations was different between monarch lineage knock-in lines and control lines (QAN, engineered control; QAN*, w1118 wild type). Symbols represent the mean ± s.e.m. of 3–6 biological replicates. Curves were fit through a univariate logistic regression (the effect of ouabain concentration on survival), and a difference in survivorship trajectories between each pair of fly lines (genotypes) was evaluated by performing an LRT to assess the significance of the inclusion of an interaction term between genotype and ouabain concentration in the logistic regression for a pair of lines (*P < 0.05, **P < 0.01, ***P < 0.001). Further information on experimental design and statistical test results is in the Source Data.

Source data

Extended Data Fig. 6 The mutational path of ATPα in the monarch butterfly lineage increases dietary tolerance to ouabain in vivo in engineered Drosophila without affecting feeding rate.

a, Estimation of mean lifespan (days) in adult females (four to seven days old at the start of the experiment) of the knock-in and control lines in CAFE assays across a range of ouabain concentrations. Each data point represents the mean ± s.e.m. of five biological replicates. Both ouabain concentration and genotype affect the survival time (two-way ANOVA (P < 0.0001) with post hoc Tukey’s tests (letters indicate pairwise differences between genotypes)). b, Estimation of LD50 (μl per fly; solid lines) and feeding rates (μl per fly per day; dashed lines) in the same individuals as in a. Each data point represents the mean ± s.e.m. of five biological replicates. Both ouabain concentration and genotype affect LD50 (two-way ANOVA (P < 0.0001) with post hoc Tukey’s tests (letters indicate pairwise differences between genotypes)). Further information on experimental design and statistical test results is in the Source Data.

Source data

Extended Data Fig. 7 Survival of knock-in lines on fly diets supplemented with dried, pulverized leaves of two milkweed species that host monarch butterflies in nature (A. curassavica and A. fascicularis).

a, Photograph of A. curassavica plant used in this study. b, c, Percentages of pupariating larvae and emerging adults of the knock-in and control (wild-type w1118: QAN*) lines on fly diet with and without A. curassavica leaf material (n = 3–4, mean ± s.e.m.). b′, c′, Differences in pupariation and emergence percentages on a fly diet with milkweed relative to percentages on a control diet (n = 3–4, mean ± s.e.m.). Mean differences between percentages in b′ and c′ were tested with one-way ANOVA (P < 0.01) followed by post hoc Tukey’s tests (letters). These panels accompany Fig. 2e. d, Photograph of A. fascicularis plant used in this study. e, f, Percentages of pupariating larvae and emerging adults of the knock-in and control lines on fly diet with and without A. fascicularis leaf material (n = 4, mean ± s.e.m.). e′, f′, Differences in pupariation and emergence percentages on a fly diet with milkweed relative to percentages on a control diet (n = 4, mean ± s.e.m.). Mean differences between percentages in e′ and f′ were tested with one-way ANOVA (P < 0.001) followed by post hoc Tukey’s tests (letters). Experiments were performed once, and adding leaf material of either of the two milkweed species to the fly diet had largely consistent effects on survival of the monarch lineage knock-in and control fly lines.

Source data

Extended Data Fig. 8 The Atpα genotypes found along the monarch lineage sequentially increase TSI to ouabain without affecting baseline levels of sodium pump activity.

a, In vitro ouabain sensitivity of engineered Drosophila Na+/K+-ATPases transiently expressed in Sf9 cell lines. Each data point represents the mean ± s.e.m. of three biological replicates. log10[IC50] for each type of Na+/K+-ATPase was estimated after four-parameter logistic curve fitting, and statistical differences between log10[IC50] values were tested with one-way ANOVA (P < 0.0001) followed by post hoc Tukey’s tests (letters). b, None of the sequential Atpα genotypes found along the monarch lineage affected baseline levels of pump activity in the enzymatic assay with extracts of transiently transfected Sf9 cells (one-way ANOVA, P = 0.3197). Each data point represents the mean ± s.e.m. of three biological replicates.

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Extended Data Fig. 9 Molecular docking simulations show stepwise reductions in ouabain binding to Na+/K+-ATPases with monarch lineage substitutions in ATPα.

The ouabain binding pocket structure obtained from molecular docking simulations for each Na+/K+-ATPase with mutated ATPα. The mutated residues are shown in sticks and are labelled. The H1–H2 loop of ATPα is shown in blue. The extracellular region of the α-subunit is removed for simplicity. For the wild-type (QAN) ATPase, ouabain is shown in its co-crystal structure coordinates (white, transparent) together with its best-docked position. For all other ATPases only the best-docked positions (closest to the co-crystal structure) are shown together with ouabain’s docking position for the wild-type ATPase (dark grey). The triple-mutated VSH ATPase has two distinct docking scores: one is similar to the docking energy for the wild-type ATPase and the other has the lowest binding energy compared to all other mutated ATPases. The potential existence of both states might be related to a trend of reduced bang sensitivity for this genotype compared to some of the single-mutant genotypes. A119 is not directly part of the ouabain binding pocket, and therefore, A119S alone does not change ouabain binding. Although the consequences of A119S are relatively subtle, the mutation may disrupt the local hydrogen bonding network and cause structural or dynamic changes in the loop or in its vicinity.

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Karageorgi, M., Groen, S.C., Sumbul, F. et al. Genome editing retraces the evolution of toxin resistance in the monarch butterfly. Nature 574, 409–412 (2019). https://doi.org/10.1038/s41586-019-1610-8

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