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Decomposing virulence to understand bacterial clearance in persistent infections

Author

Listed:
  • Beatriz Acuña Hidalgo

    (Freie Universität Berlin)

  • Luís M. Silva

    (Freie Universität Berlin
    University of Neuchâtel)

  • Mathias Franz

    (Freie Universität Berlin)

  • Roland R. Regoes

    (Institute of Integrative Biology, ETH Zurich)

  • Sophie A. O. Armitage

    (Freie Universität Berlin)

Abstract

Following an infection, hosts cannot always clear the pathogen, instead either dying or surviving with a persistent infection. Such variation is ecologically and evolutionarily important because it can affect infection prevalence and transmission, and virulence evolution. However, the factors causing variation in infection outcomes, and the relationship between clearance and virulence are not well understood. Here we show that sustained persistent infection and clearance are both possible outcomes across bacterial species showing a range of virulence in Drosophila melanogaster. Variation in virulence arises because of differences in the two components of virulence: bacterial infection intensity inside the host (exploitation), and the amount of damage caused per bacterium (per parasite pathogenicity). As early-phase exploitation increased, clearance rates later in the infection decreased, whereas there was no apparent effect of per parasite pathogenicity on clearance rates. Variation in infection outcomes is thereby determined by how virulence – and its components – relate to the rate of pathogen clearance. Taken together we demonstrate that the virulence decomposition framework is broadly applicable and can provide valuable insights into host-pathogen interactions.

Suggested Citation

  • Beatriz Acuña Hidalgo & Luís M. Silva & Mathias Franz & Roland R. Regoes & Sophie A. O. Armitage, 2022. "Decomposing virulence to understand bacterial clearance in persistent infections," Nature Communications, Nature, vol. 13(1), pages 1-14, December.
    • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-32118-1
    DOI: 10.1038/s41467-022-32118-1
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    References listed on IDEAS

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    3. Wickham, Hadley, 2011. "The Split-Apply-Combine Strategy for Data Analysis," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 40(i01).
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