Abstract

CRISPR/Cas9 provides a highly efficient and flexible genome editing technology with numerous potential applications ranging from gene therapy to population control. Some proposed applications involve the integration of CRISPR/Cas9 endonucleases into an organism's genome, which raises questions about potentially harmful effects to the transgenic individuals. One example for which this is particularly relevant are CRISPR-based gene drives conceived for the genetic alteration of entire populations. The performance of such drives can strongly depend on fitness costs experienced by drive carriers, yet relatively little is known about the magnitude and causes of these costs. Here, we assess the fitness effects of genomic CRISPR/Cas9 expression in Drosophila melanogaster cage populations by tracking allele frequencies of four different transgenic constructs that allow us to disentangle 'direct' fitness costs due to the integration, expression, and target-site activity of Cas9, from fitness costs due to potential off-target cleavage. Using a maximum likelihood framework, we find that a model with no direct fitness costs but moderate costs due to off-target effects fits our cage data best. Consistent with this, we do not observe fitness costs for a construct with Cas9HF1, a high-fidelity version of Cas9. We further demonstrate that using Cas9HF1 instead of standard Cas9 in a homing drive achieves similar drive conversion efficiency. These results suggest that gene drives should be designed with high-fidelity endonucleases and may have implications for other applications that involve genomic integration of CRISPR endonucleases.

Data availability

All data generated or analyzed during this study are included in the manuscript and supporting files.

Article and author information

Author details

  1. Anna M Langmüller

    Department of Computational Biology, Cornell University, Ithaca, United States
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-6102-8862
  2. Jackson Champer

    Center for Bioinformatics, Peking University, Beijing, China
    For correspondence
    jchamper@pku.edu.cn
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-3814-3774
  3. Sandra Lapinska

    Department of Computational Biology, Cornell University, Ithaca, United States
    Competing interests
    No competing interests declared.
  4. Lin Xie

    Department of Computational Biology, Cornell University, Ithaca, United States
    Competing interests
    No competing interests declared.
  5. Matthew Metzloff

    Department of Computational Biology, Cornell University, Ithaca, United States
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-6108-5031
  6. Samuel E Champer

    Department of Computational Biology, Cornell University, Ithaca, United States
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-4559-7627
  7. Jingxian Liu

    Department of Biological Statistics, Cornell University, Ithaca, United States
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-2172-3297
  8. Yineng Xu

    Department of Molecular Biology and Genetics, Cornell University, Ithaca, United States
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-4473-4052
  9. Jie Du

    Center for Bioinformatics, Peking University, Beijing, China
    Competing interests
    No competing interests declared.
  10. Andrew G Clark

    Department of Computational Biology, Cornell University, Ithaca, United States
    Competing interests
    No competing interests declared.
  11. Philipp W Messer

    Department of Computational Biology, Cornell University, Ithaca, United States
    For correspondence
    messer@cornell.edu
    Competing interests
    Philipp W Messer, Reviewing editor, eLife.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-8453-9377

Funding

National Institutes of Health (R21AI130635)

  • Jackson Champer
  • Andrew G Clark
  • Philipp W Messer

National Institutes of Health (F32AI138476)

  • Jackson Champer

National Institutes of Health (R01GM127418)

  • Philipp W Messer

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

Ethics

Animal experimentation: All flies with an active homing gene drive system were kept at the Sarkaria Arthropod Research Laboratory at Cornell University under Arthropod Containment Level 2 protocols in accordance with USDA APHIS standards. All safety standards were approved by the Cornell University Institutional Biosafety Committee.

Copyright

© 2022, Langmüller 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

  • 1,149
    views
  • 232
    downloads
  • 10
    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. Anna M Langmüller
  2. Jackson Champer
  3. Sandra Lapinska
  4. Lin Xie
  5. Matthew Metzloff
  6. Samuel E Champer
  7. Jingxian Liu
  8. Yineng Xu
  9. Jie Du
  10. Andrew G Clark
  11. Philipp W Messer
(2022)
Fitness effects of CRISPR endonucleases in Drosophila melanogaster populations
eLife 11:e71809.
https://doi.org/10.7554/eLife.71809

Share this article

https://doi.org/10.7554/eLife.71809

Further reading

    1. Computational and Systems Biology
    2. Evolutionary Biology
    Pierre Barrat-Charlaix, Richard A Neher
    Research Article

    As pathogens spread in a population of hosts, immunity is built up, and the pool of susceptible individuals are depleted. This generates selective pressure, to which many human RNA viruses, such as influenza virus or SARS-CoV-2, respond with rapid antigenic evolution and frequent emergence of immune evasive variants. However, the host’s immune systems adapt, and older immune responses wane, such that escape variants only enjoy a growth advantage for a limited time. If variant growth dynamics and reshaping of host-immunity operate on comparable time scales, viral adaptation is determined by eco-evolutionary interactions that are not captured by models of rapid evolution in a fixed environment. Here, we use a Susceptible/Infected model to describe the interaction between an evolving viral population in a dynamic but immunologically diverse host population. We show that depending on strain cross-immunity, heterogeneity of the host population, and durability of immune responses, escape variants initially grow exponentially, but lose their growth advantage before reaching high frequencies. Their subsequent dynamics follows an anomalous random walk determined by future escape variants and results in variant trajectories that are unpredictable. This model can explain the apparent contradiction between the clearly adaptive nature of antigenic evolution and the quasi-neutral dynamics of high-frequency variants observed for influenza viruses.

    1. Ecology
    2. Evolutionary Biology
    Rebecca D Tarvin, Jeffrey L Coleman ... Richard W Fitch
    Research Article

    Understanding the origins of novel, complex phenotypes is a major goal in evolutionary biology. Poison frogs of the family Dendrobatidae have evolved the novel ability to acquire alkaloids from their diet for chemical defense at least three times. However, taxon sampling for alkaloids has been biased towards colorful species, without similar attention paid to inconspicuous ones that are often assumed to be undefended. As a result, our understanding of how chemical defense evolved in this group is incomplete. Here, we provide new data showing that, in contrast to previous studies, species from each undefended poison frog clade have measurable yet low amounts of alkaloids. We confirm that undefended dendrobatids regularly consume mites and ants, which are known sources of alkaloids. Thus, our data suggest that diet is insufficient to explain the defended phenotype. Our data support the existence of a phenotypic intermediate between toxin consumption and sequestration — passive accumulation — that differs from sequestration in that it involves no derived forms of transport and storage mechanisms yet results in low levels of toxin accumulation. We discuss the concept of passive accumulation and its potential role in the origin of chemical defenses in poison frogs and other toxin-sequestering organisms. In light of ideas from pharmacokinetics, we incorporate new and old data from poison frogs into an evolutionary model that could help explain the origins of acquired chemical defenses in animals and provide insight into the molecular processes that govern the fate of ingested toxins.