Health trajectories reveal the dynamic contributions of host genetic resistance and tolerance to infection outcome

Author:

Lough Graham1,Kyriazakis Ilias2,Bergmann Silke3,Lengeling Andreas4,Doeschl-Wilson Andrea B.1

Affiliation:

1. Genetics and Genomics Division, The Roslin Institute and R(D)SVS, University of Edinburgh, Edinburgh, UK

2. School of Agriculture, Food and Rural Development, Newcastle University, Newcastle upon Tyne, UK

3. Department of Infection Genetics, Helmholtz Centre for Infection Research, Braunschweig, Germany

4. Infection and Immunity Division, The Roslin Institute and R(D)SVS, University of Edinburgh, Edinburgh, UK

Abstract

Resistance and tolerance are two alternative strategies hosts can adopt to survive infections. Both strategies may be genetically controlled. To date, the relative contribution of resistance and tolerance to infection outcome is poorly understood. Here, we use a bioluminescent Listeria monocytogenes ( Lm ) infection challenge model to study the genetic determination and dynamic contributions of host resistance and tolerance to listeriosis in four genetically diverse mouse strains. Using conventional statistical analyses, we detect significant genetic variation in both resistance and tolerance, but cannot capture the time-dependent relative importance of either host strategy. We overcome these limitations through the development of novel statistical tools to analyse individual infection trajectories portraying simultaneous changes in infection severity and health. Based on these tools, early expression of resistance followed by expression of tolerance emerge as important hallmarks for surviving Lm infections. Our trajectory analysis further reveals that survivors and non-survivors follow distinct infection paths (which are also genetically determined) and provides new survival thresholds as objective endpoints in infection experiments. Future studies may use trajectories as novel traits for mapping and identifying genes that control infection dynamics and outcome. A M atlab script for user-friendly trajectory analysis is provided.

Funder

EUMODIC

National German Genome Network

Genus

Biotechnology and Biological Sciences Research Council

Higher Education Funding Council for England

SYSGENET

Publisher

The Royal Society

Subject

General Agricultural and Biological Sciences,General Environmental Science,General Immunology and Microbiology,General Biochemistry, Genetics and Molecular Biology,General Medicine

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