The study of infectious disease has been aided by model organisms, which have helped to elucidate molecular mechanisms and contributed to the development of new treatments; however, the lack of a conceptual framework for unifying findings across models, combined with host variability, has impeded progress and translation. Here, we fill this gap with a simple graphical and mathematical framework to study disease tolerance, the dose response curve relating health to microbe load; this approach helped uncover parameters that were previously overlooked. Using a model experimental system in which we challenged Drosophila melanogaster with the pathogen Listeria monocytogenes, we tested this framework, finding that microbe growth, the immune response, and disease tolerance were all well represented by sigmoid models. As we altered the system by varying host or pathogen genetics, disease tolerance varied, as we would expect if it was indeed governed by parameters controlling the sensitivity of the system (the number of bacteria required to trigger a response) and maximal effect size according to a logistic equation. Though either the pathogen or host immune response or both together could theoretically be the proximal cause of pathology that killed the flies, we found that the pathogen, but not the immune response, drove damage in this model. With this new understanding of the circuitry controlling disease tolerance, we can now propose better ways of choosing, combining, and developing treatments.

Many taxa exhibit plastic immune responses initiated after primary microbial exposure that provide increased protection against disease-induced mortality and the fitness costs of infection. In several arthropod species, this protection can even be passed from parents to offspring through a phenomenon called trans-generational immune priming. Here, we first demonstrate that trans-generational priming is a repeatable phenomenon in flour beetles (Tribolium castaneum) primed and infected with Bacillus thuringiensis (Bt). We then quantify the within-host dynamics of microbes and host physiological responses in infected offspring from primed and unprimed mothers by monitoring bacterial density and using mRNA-seq to profile host gene expression, respectively, over the acute infection period. We find that priming increases inducible resistance against Bt around a critical temporal juncture where host septicemic trajectories, and consequently survival, may be determined in unprimed individuals. Our results identify a highly differentially expressed biomarker of priming, containing an EIF4-e domain, in uninfected individuals, as well as several other candidate genes. Moreover, the induction and decay dynamics of gene expression over time suggest a metabolic shift in primed individuals. The identified bacterial and gene expression dynamics are likely to influence patterns of bacterial fitness and disease transmission in natural populations.

Infected hosts differ in their responses to pathogens; some hosts are resilient and recover their original health, whereas others follow a divergent path and die. To quantitate these differences, we propose mapping the routes infected individuals take through “disease space.” We find that when plotting physiological parameters against each other, many pairs have hysteretic relationships that identify the current location of the host and predict the future route of the infection. These maps can readily be constructed from experimental longitudinal data, and we provide two methods to generate the maps from the cross-sectional data that is commonly gathered in field trials. We hypothesize that resilient hosts tend to take small loops through disease space, whereas nonresilient individuals take large loops. We support this hypothesis with experimental data in mice infected with Plasmodium chabaudi, finding that dying mice trace a large arc in red blood cells (RBCs) by reticulocyte space as compared to surviving mice. We find that human malaria patients who are heterozygous for sickle cell hemoglobin occupy a small area of RBCs by reticulocyte space, suggesting this approach can be used to distinguish resilience in human populations. This technique should be broadly useful in describing the in-host dynamics of infections in both model hosts and patients at both population and individual levels.

The immune response of a host can have important impacts on host‐pathogen interactions, but investment in immunity often changes dynamically across the life history of a host. One form of investment involves the induction of a primed immune response against previously encountered pathogens that protects the host from re‐infection. In addition to providing immediate protective effects, immune priming can also provide two types of ‘delayed’ protection against pathogens: priming across life stages (ontogenic priming) and priming across generations (trans‐generational priming). Consequently both types of immune priming have the potential to mediate life history variability in host–pathogen interactions, which could have important consequences for disease prevalence and dynamics as well as for the demographic structure of the host population. Here we develop a stage‐structured SIRS model for an invertebrate host to explore the relative and combined impact of ontogenic priming and trans‐generational priming on infection prevalence, host population size, and population age structure. Our model predicts that both types of immune priming can dramatically reduce disease prevalence at equilibrium, but their individual and combined effects on population size and age structure depend on the magnitude of tradeoffs between immune protection and reproduction as well as on the symmetry of infection parameters between life stages. This model underscores the potential importance of life‐history based immune investment patterns for disease dynamics and highlights the need for wide‐spread empirical estimation of parameters that represent the maintenance of immune priming in insects.

Cooperation and cheating are widespread evolutionary strategies. While cheating confers an advantage to individual entities within a group, competition between groups favors cooperation. Selfish or cheater mitochondrial DNA (mtDNA) proliferates within hosts while being selected against at the level of host fitness. How does environment shape cheater dynamics across different selection levels? Focusing on food availability, we address this question using heteroplasmic Caenorhabditis elegans. We find that the proliferation of selfish mtDNA within hosts depends on nutrient status stimulating mtDNA biogenesis in the developing germline. Interestingly, mtDNA biogenesis is not sufficient for this proliferation, which also requires the stress-response transcription factor FoxO/DAF-16. At the level of host fitness, FoxO/DAF-16 also prevents food scarcity from accelerating the selection against selfish mtDNA. This suggests that the ability to cope with nutrient stress can promote host tolerance of cheaters. Our study delineates environmental effects on selfish mtDNA dynamics at different levels of selection.