A within-host infection model to explore tolerance and resistance
Abstract
How are some individuals surviving infections while others die? The answer lies in how infected individuals invest into controlling pathogen proliferation and mitigating damage, two strategies respectively called resistance and disease tolerance. Pathogen within-host dynamics (WHD), influenced by resistance, and its connection to host survival, determined by tolerance, decide the infection outcome. To grasp these intricate effects of resistance and tolerance, we used a deterministic theoretical model where pathogens interact with the immune system of a host. The model describes the positive and negative regulation of the immune response, consider the way damage accumulate during the infection and predicts WHD. When chronic, infections stabilize at a Set-Point Pathogen Load (SPPL). Our model predicts that this situation can be transient, the SPPL being then a predictor of life span which depends on initial condition (e.g. inoculum). When stable, the SPPL is rather diagnostic of non lethal chronic infections. In lethal infections, hosts die at a Pathogen Load Upon Death (PLUD) which is almost independent from the initial conditions. As the SPPL, the PLUD is affected by both resistance and tolerance but we demonstrate that it can be used in conjunction with mortality measurement to distinguish the effect of disease tolerance from that of resistance. We validate empirically this new approach, using Drosophila melanogaster and the pathogen Providencia rettgeri. We found that, as predicted by the model, hosts that were wounded or deficient of key antimicrobial peptides had a higher PLUD, while Catalase mutant hosts, likely to have a default in disease tolerance, had a lower PLUD.
Data availability
Details of the shiny application can be found on Zenodo at P. Lafont. (2024). Sydag/WHD\_shiny: Shiny\_WHD\_model (Main\_release). Zenodo. \href{https://doi.org/10.5281/zenodo.13309654}{https://doi.org/10.5281/zenodo.13309654}. Scripts and analyses are available via a Rmarkdown HTML file provided as supplementary file. Data are available as supplementary files.
Article and author information
Author details
Funding
Agence Nationale de la Recherche (ANR- 10-LABX-41)
- David Duneau
Agence Nationale de la Recherche (ANR-11-IDEX-0002-02)
- Jean-Baptiste Ferdy
Agence Nationale de la Recherche (LIA BEEG-B)
- David Duneau
National Institutes of Health (5R01AI148541-05)
- Nicolas Buchon
National Science Foundation (IOS 1398682)
- Nicolas Buchon
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Copyright
© 2025, Duneau et al.
This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.
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