Fitness effects of CRISPR endonucleases in Drosophila melanogaster populations

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.

Reviewing Editor

  1. Alekos Simoni

Publication history

  1. Received: June 30, 2021
  2. Accepted: September 8, 2022
  3. Accepted Manuscript published: September 22, 2022 (version 1)

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

  • 168
    Page views
  • 60
    Downloads
  • 0
    Citations

Article citation count generated by polling the highest count across the following sources: Crossref, PubMed Central, Scopus.

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

Further reading

    1. Ecology
    2. Evolutionary Biology
    Dakota E McCoy, Benjamin Goulet-Scott ... John Kartesz
    Tools and Resources

    Sustainable cities depend on urban forests. City trees-pillars of urban forests - improve our health, clean the air, store CO2, and cool local temperatures. Comparatively less is known about city tree communities as ecosystems, particularly regarding spatial composition, species diversity, tree health, and the abundance of introduced species. Here, we assembled and standardized a new dataset of N=5,660,237 trees from 63 of the largest US cities with detailed information on location, health, species, and whether a species is introduced or naturally occurring (i.e., 'native'). We further designed new tools to analyze spatial clustering and the abundance of introduced species. We show that trees significantly cluster by species in 98% of cities, potentially increasing pest vulnerability (even in species-diverse cities). Further, introduced species significantly homogenize tree communities across cities, while naturally occurring trees (i.e., 'native' trees) comprise 0.51%-87.3% (median=45.6%) of city tree populations. Introduced species are more common in drier cities, and climate also shapes tree species diversity across urban forests. Parks have greater tree species diversity than urban settings. Compared to past work which focused on canopy cover and species richness, we show the importance of analyzing spatial composition and introduced species in urban ecosystems (and we develop new tools and datasets to do so). Future work could analyze city trees and socio-demographic variables or bird, insect, and plant diversity (e.g., from citizen-science initiatives). With these tools, we may evaluate existing city trees in new, nuanced ways and design future plantings to maximize resistance to pests and climate change. We depend on city trees.

    1. Evolutionary Biology
    2. Genetics and Genomics
    Laura Katharine Hayward, Guy Sella
    Research Article

    Polygenic adaptation is thought to be ubiquitous, yet remains poorly understood. Here, we model this process analytically, in the plausible setting of a highly polygenic, quantitative trait that experiences a sudden shift in the fitness optimum. We show how the mean phenotype changes over time, depending on the effect sizes of loci that contribute to variance in the trait, and characterize the allele dynamics at these loci. Notably, we describe the two phases of the allele dynamics: The first is a rapid phase, in which directional selection introduces small frequency differences between alleles whose effects are aligned with or opposed to the shift, ultimately leading to small differences in their probability of fixation during a second, longer phase, governed by stabilizing selection. As we discuss, key results should hold in more general settings, and have important implications for efforts to identify the genetic basis of adaptation in humans and other species.