Adaptive substitutions underlying cardiac glycoside insensitivity in insects exhibit epistasis in vivo

  1. Andrew M Taverner
  2. Lu Yang
  3. Zachary J Barile
  4. Becky Lin
  5. Julie Peng  Is a corresponding author
  6. Ana P Pinharanda
  7. Arya S Rao
  8. Bartholomew P Roland
  9. Aaron D Talsma
  10. Daniel Wei
  11. Georg Petschenka
  12. MIchael J Palladino  Is a corresponding author
  13. Peter Andolfatto  Is a corresponding author
  1. Princeton University, United States
  2. University of Pittsburgh, United States
  3. Columbia University, United States
  4. Justus-Liebig-Universität Gießen, Germany

Abstract

Predicting how species will respond to selection pressures requires understanding the factors that constrain their evolution. We use genome engineering of Drosophila to investigate constraints on the repeated evolution of unrelated herbivorous insects to toxic cardiac glycosides, which primarily occurs via a small subset of possible functionally-relevant substitutions to Na+,K+-ATPase. Surprisingly, we find that frequently observed adaptive substitutions at two sites, 111 and 122, are lethal when homozygous and adult heterozygotes exhibit dominant neural dysfunction. We identify a phylogenetically correlated substitution, A119S, that partially ameliorates the deleterious effects of substitutions at 111 and 122. Despite contributing little to cardiac glycoside-insensitivity in vitro, A119S, like substitutions at 111 and 122, substantially increases adult survivorship upon cardiac glycoside exposure. Our results demonstrate the importance of epistasis in constraining adaptive paths. Moreover, by revealing distinct effects of substitutions in vitro and in vivo, our results underscore the importance of evaluating the fitness of adaptive substitutions and their interactions in whole organisms.

Data availability

Sequence data as been deposited in Genbank and the details of all accession numbers of this and previously published data are tabulated in Supplementary Table S1.

The following data sets were generated

Article and author information

Author details

  1. Andrew M Taverner

    Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-8265-6836
  2. Lu Yang

    Department of Ecology and Evolutionary Biology, Princeton University, Princeton, United States
    Competing interests
    The authors declare that no competing interests exist.
  3. Zachary J Barile

    Department of Pharmacology and Chemical Biology, University of Pittsburgh, Pittsburgh, United States
    Competing interests
    The authors declare that no competing interests exist.
  4. Becky Lin

    Department of Pharmacology and Chemical Biology, University of Pittsburgh, Pittsburgh, United States
    Competing interests
    The authors declare that no competing interests exist.
  5. Julie Peng

    Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, United States
    For correspondence
    jzpeng@Princeton.edu
    Competing interests
    The authors declare that no competing interests exist.
  6. Ana P Pinharanda

    Department of Biological Sciences, Columbia University, New York, United States
    Competing interests
    The authors declare that no competing interests exist.
  7. Arya S Rao

    Department of Biological Sciences, Columbia University, New York, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-3007-4812
  8. Bartholomew P Roland

    Department of Pharmacology and Chemical Biology, University of Pittsburgh, Pittsburgh, United States
    Competing interests
    The authors declare that no competing interests exist.
  9. Aaron D Talsma

    Department of Pharmacology and Chemical Biology, University of Pittsburgh, Pittsburgh, United States
    Competing interests
    The authors declare that no competing interests exist.
  10. Daniel Wei

    Department of Pharmacology and Chemical Biology, University of Pittsburgh, Pittsburgh, United States
    Competing interests
    The authors declare that no competing interests exist.
  11. Georg Petschenka

    Institute for Insect Biotechnology, Justus-Liebig-Universität Gießen, Hesse, Germany
    Competing interests
    The authors declare that no competing interests exist.
  12. MIchael J Palladino

    Department of Pharmacology and Chemical Biology, University of Pittsburgh, Pittsbugh, United States
    For correspondence
    mjp44@pitt.edu
    Competing interests
    The authors declare that no competing interests exist.
  13. Peter Andolfatto

    Department of Biological Sciences, Columbia University, New York, United States
    For correspondence
    pa2543@columbia.edu
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-3393-4574

Funding

National Institutes of Health (R01 GM115523)

  • Peter Andolfatto

National Institutes of Health (T32 GM008424)

  • Bartholomew P Roland

National Institutes of Health (R01 GM108073)

  • MIchael J Palladino

National Institutes of Health (R01 AG027453)

  • MIchael J Palladino

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Copyright

© 2019, Taverner et al.

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.

Metrics

  • 4,013
    views
  • 405
    downloads
  • 32
    citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.

Download links

A two-part list of links to download the article, or parts of the article, in various formats.

Downloads (link to download the article as PDF)

Open citations (links to open the citations from this article in various online reference manager services)

Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)

  1. Andrew M Taverner
  2. Lu Yang
  3. Zachary J Barile
  4. Becky Lin
  5. Julie Peng
  6. Ana P Pinharanda
  7. Arya S Rao
  8. Bartholomew P Roland
  9. Aaron D Talsma
  10. Daniel Wei
  11. Georg Petschenka
  12. MIchael J Palladino
  13. Peter Andolfatto
(2019)
Adaptive substitutions underlying cardiac glycoside insensitivity in insects exhibit epistasis in vivo
eLife 8:e48224.
https://doi.org/10.7554/eLife.48224

Share this article

https://doi.org/10.7554/eLife.48224

Further reading

    1. Evolutionary Biology
    Mattias Siljestam, Claus Rueffler
    Research Article

    The majority of highly polymorphic genes are related to immune functions and with over 100 alleles within a population, genes of the major histocompatibility complex (MHC) are the most polymorphic loci in vertebrates. How such extraordinary polymorphism arose and is maintained is controversial. One possibility is heterozygote advantage (HA), which can in principle maintain any number of alleles, but biologically explicit models based on this mechanism have so far failed to reliably predict the coexistence of significantly more than ten alleles. We here present an eco-evolutionary model showing that evolution can result in the emergence and maintenance of more than 100 alleles under HA if the following two assumptions are fulfilled: first, pathogens are lethal in the absence of an appropriate immune defence; second, the effect of pathogens depends on host condition, with hosts in poorer condition being affected more strongly. Thus, our results show that HA can be a more potent force in explaining the extraordinary polymorphism found at MHC loci than currently recognized.

    1. Cancer Biology
    2. Evolutionary Biology
    Arman Angaji, Michel Owusu ... Johannes Berg
    Research Article

    In growing cell populations such as tumours, mutations can serve as markers that allow tracking the past evolution from current samples. The genomic analyses of bulk samples and samples from multiple regions have shed light on the evolutionary forces acting on tumours. However, little is known empirically on the spatio-temporal dynamics of tumour evolution. Here, we leverage published data from resected hepatocellular carcinomas, each with several hundred samples taken in two and three dimensions. Using spatial metrics of evolution, we find that tumour cells grow predominantly uniformly within the tumour volume instead of at the surface. We determine how mutations and cells are dispersed throughout the tumour and how cell death contributes to the overall tumour growth. Our methods shed light on the early evolution of tumours in vivo and can be applied to high-resolution data in the emerging field of spatial biology.