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

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. A bioluminescent Listeria monocytogenes (Lm) infection challenge model on four genetically diverse mouse strains was used to study the genetic determination and dynamic contributions of host resistance and tolerance to listeriosis, a serious food-borne infectious disease in humans. Conventional statistical analyses revealed significant genetic variation in both resistance and tolerance, but could not capture the time-dependent relative importance of either strategy. These limitations were overcome by 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 later by expression of tolerance, emerged as important hallmarks for surviving Lm infections. Trajectory analysis further revealed that survivors and non-survivors follow distinct infection paths, which are also genetically determined, and provided 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 Matlab script for user-friendly trajectory analysis is provided.

宿主在感染过程中存活可采取两种备选策略：抗性（resistance）与耐受（tolerance），且这两种策略均可受遗传调控。截至目前，学界对于抗性与耐受对感染结局的相对贡献仍知之甚少。本研究采用基于四种遗传多样性小鼠品系的生物发光单核细胞增生李斯特菌（Listeria monocytogenes，简称Lm）感染攻击模型，探究宿主抗性与耐受对李斯特菌病——一种严重的人类食源性传染病的遗传决定机制及动态贡献。传统统计分析显示，抗性与耐受均存在显著的遗传变异，但无法捕捉两种策略随时间变化的相对重要性。为克服上述局限，本研究开发了新型统计工具，用于分析同时反映感染严重程度与健康状态变化的个体感染轨迹。基于该工具，研究发现先启动抗性表达、后续再启动耐受表达，是Lm感染存活的重要特征。轨迹分析进一步揭示，存活者与非存活者具有截然不同的感染路径，且该路径同样受遗传调控；同时本研究还确定了新的存活阈值，可作为感染实验中的客观终点指标。未来研究可将感染轨迹作为新型性状，用于定位并鉴定调控感染动态与结局的相关基因。本研究附带了一款便于用户使用的轨迹分析Matlab脚本。